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• Course title: Solid State PhysicsÌý
• Number of ECTS: 6
• Course code: MCMP-64
• Module(s): Common core
• Language: EN
• Mandatory: Yes

The course introduces the students to the atomic and electronic structure of solid crystalline materials. The goal of solid-state physics is to understand the macroscopic properties (such as hardness, color, electrical conductivity, heat capacity, etc.) from the microscopic structure of the material. The lattice dynamics (phonons) of crystalline materials will be studied in oder to understand the thermal properties of matter. The electronic structure of metals, semiconductors, and insulators will be treated in detail, as well as their optical properties.

A student who passes this course will be able to:
– explain the most common crystal structures and their determination by X-ray scattering
– describe the reaction of crystals to various stresses
– understand the storage and transport of heat in solids
– explain the difference between metals, semiconductors and insulators based on their electronic structure
– understand the link between optical properties and electronic excitations
-understand the basic concepts that dictate superconductivity

The course will enable the student to study the literature on current research topics in the field of solid-state physics.

crystal structures (reciprocal lattice, X-ray diffraction, crystal bonds, crystal defects)
elastic properties (continuum mechanics, elastic tensors)
phonons (quantisation, dispersion, Debye and Einstein model, specific heat and heat conduction)
electrons (band structure, Sommerfeld model, Bloch functions, quasi free electrons, tight binding model, defects in semiconductors)
solid state optics (model dielectric functions, electronic transitions)
superconductivity

Task 1:Ìý

Home-assignment (the student must achieve at least 50% of the total possible marks to be allowed to take the oral exam). Final oral exam at the end of the semester
Assessment rules:Ìý 80% oral exam and 20% TD mark
Mark consists of two parts: 20% TD mark + 80% oral exam mark
TD: Exercise sheet, 1 week time to solve the problems
1 exercise sheet per week
Oral finalÌý exam – entrance requires at least 50% of total points in the TD
Oral exam – 50 minutes (25min: Dale; 25min: Redinger)
Assessment criteria:Ìý QA during oral exam. Questions will be based on the content of the course. Written notes will be taken. Marks will be discussed by the two Professors after all the exams in order to assure a fair assessment of all students. Both parts (Prof. Dale Prof. Redinger) will be weighted equally.
Retake exam offeredÌý
Retake exams can only be accepted if oral exam requirement fulfilled
Retake exam – rules:
Marks from TD 50% or 10/20 minimum
Marks from TD will be carried over and 80%-20% rule still applies

Support : Lecture Slides
Literature :
– C. Kittel, Introduction to Solid State Physics, Wiley
– H. Ibach and H. Lüth, Solid-State Physics, An Introduction to Principles of Materials Science,Springer
– N.W. Ashcroft and N.D. Mermin, Solid State Physics, Saunders College Publishing
РRudolf Gross, Achim Marx, Festk̦rperphysik, Oldenbourg Verlag (in German)
– P. Yu and M. Cardona, Fundamentals of Semiconductors: Physics and Materials Properties, Springer
– K. Kopitzki, Einführung in die Festkörperphysik, Teubner (in German)
– G. Burns, Solid State Physics, Academic Press, used only

• Course title: Scientific Python
• Number of ECTS: 1
• Course code: F1_MA_MAT_MMCS2-4
• Module(s): Common core
• Language: EN
• Mandatory: Yes

• Introduction


• Getting started


• Basics of Python


• Array computations with numpy


• Array computations with numpy (cont.)


• Plotting with matplotlib


• Tabular data manipulation with pandas


• Tabular data manipulation with pandas (cont.)


• Writing good quality and robust Python code

First session

You should feel comfortable writing basic scientific programs in Python, and be able to participate fully in future courses that require an element of programming.

Retake exam
Retake exam not possible, course must be retaken.

This course covers the basics of scientific programming with Python. It is aimed at people who have done some programming before, perhaps on an undergraduate course, but need a refresher before starting their Masters or Doctoral degrees at the ³ÉÈËÍ·Ìõversity.
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A coursework will be distributed at the end of the class. To pass the course and receive the ECTS credits you must complete the coursework.

• Course title: Physics of soft andÌýcomplexÌýmatterÌý
• Number of ECTS: 6
• Course code: MCMP-66
• Module(s): Common core
• Language: EN
• Mandatory: Yes

Learn the key tools required to analyze and understand the physics of soft and complex matter, illustrated with multiple examples from liquid crystals, living matter, polymeric liquids, colloids etc.

The student will get a general introduction to physical phenomena of general importance, with particular relevance for soft and complex matter, such as thermodynamics of phase transitions and phase separation, equilibrium and non-equilibrium states of matter, enthalpic and entropic contributions to the free energy, statistical approaches to polymer physics, active matter and liquid crystals, the hydrophobic effect and amphiphilicity, interfacial/surface tension and capillary phenomena, and topological defects.

1.Ìý Phases, phase transitions & spontaneous symmetry breaking. Critical fluctuations. Landau theory and symmetry considerations (1st/ 2nd-order transitions).
2.Ìý Non-equilibrium states, the glass transition, jamming and gelation. The concepts of kinetic arrest and percolation.
3.Ìý Thermodynamics of mixing and of dissolution. Flory-Huggins theory of phase separation. Nucleation and growth vs. spinodal decomposition.
4. Equilibrium (passive) and non-equilibrium (active, dissipative) interactions, and their energy, length and time scales. Feedback mechanisms.
5.Ìý Statistical approaches: characteristics of polymers; steady state vs. fluctuating environments; large populations and active matter (biological and synthetic).
6.Ìý Transport phenomena: Random walks & diffusion, advection, viscous flow, Navier Stokes Equations and non-dimensional parameters (building on Cont. Mech. in B.Sc.), passive/active transport.
7.Ìý Scale invariance and fractal geometries in soft and living matter, in surface growth, in growth andÌý break-down phenomena, …
8.Ìý Order from disorder: entropically driven organization.
9.Ìý Self-assembly and self-organization. Liquid crystalline ordering.
10.Ìý Topological constraints and classification; deformations vs. topological transformations; topological defects and their interactions.
11.Ìý Interfacial/surface tension and capillary phenomena.
12.Ìý Physics of biopolymers: natural and synthetic nanotechnology.

Maximum one unexcused absence from the TD is allowed. If a student misses more than one they are not allowed to take the final exam.

Task 1: Active participation in the TD classes

Task 2: Mid-term written exam (part of the TD class, replacing one exercise occasion)


Assessment rules: Student must hand in solutions to the full weekly problem set ahead of class which the TD teacher grades. During class the student is expected to solve one problem at the board per TD class. During mid-term exam only pen/pencil and eraser may be used. No electronic devices are allowed nor necessary.


Assessment criteria: The total TD score is a weighted average of the components as follows: 60% mid-term exam score, 25% participation during TD classes (as scored by TD teacher), 15% score on handed-in solutions.Ìý


Task 3: Final oral exam, with TD and CM teachers present together with the student.


Assessment rules: The CM teacher will ask questions focusing on explaining concepts, covering all aspects of the course. The student is not expected to solve specific problems but rather demonstrate a general understanding. The student may answer orally and by drawing/writing at the white-/blackboard. Each session takes 30-50 minutes depending on succinctness of the answers.


Assessment criteria: The final course score is calculated as a weighted average of the final exam score (2/3) and the TD score (1/3).


In case a student has passed the TD component but fails the course overall, another oral re-take exam is offered during the next exam period. This will be assessed as the original final exam (Task 3).

PDF of lecture slides will be shared after (not before) lectures. Recommended books:Ìý Soft Condensed Matter (Richard Jones), Soft Matter Physics (Masao Doi) and Soft Matter: Concepts, Phenomena and Applications (Wim van Saarloos, Vincenzo Vitelli and Zorana Zeravcic)

• Course title: Computational methodsÌý
• Number of ECTS: 5
• Course code: MCMP-65
• Module(s): Common core
• Language: EN
• Mandatory: Yes

• Course title: Magnetism and superconductivityÌý
• Number of ECTS: 6
• Course code: MCMP-58
• Module(s): Photovoltaics and energy materials I
• Language: EN
• Mandatory: No

The lecture aims at

• introducing the student to the basic principles of magnetism, magnetic materials, and superconductivity


• applying these principles to problems in fundamental and applied magnetism and superconductivity


• developing the ability to critically assess certain issues in the field

After completion of the course, the student is expected to understand and explain

• the basic quantities in magnetism and superconductivity (magnetic moments, magnetic fields, magnetization, units, etc.)the basic phenomenology and the various manifestations of magnetism (hysteresis loop, domains and domain walls, spontaneous magnetization, Dia-, Para-, Superpara, Ferro-, Antiferro-, Ferrimagnetism, etc.)


• the basic magnetic interactions and anisotropies (exchange interaction, magnetocrystalline anisotropy, magnetostriction, magnetodipolar interaction, Zeeman interaction, etc.)


• simple models for magnetism (Mean-Field theory, Curie-Weiss law, Bloch law (magnons), Stoner-Wohlfarth model, micromagnetism, etc.)


• the basics of the main experimental techniques (ac+dc magnetization, neutron scattering, spin-polarized STM, Kerr microscopy, Lorentz microscopy, magnetic force microscopy, Mössbauer spectrocopy, etc.)


• the basics of magnetic materials (hard and soft magnets, ferrofluids, nanoparticles, thin films and multilayer structures, etc.)


• to understand and explain the basic phenomenology of superconductivity

Syllabus

• Introduction
  • Basic Quantities (magnetic moment, orbital and spin angular momentum, Hund rules, …)
  • ³ÉÈËÍ·Ìõts, Fields, …
  • Bohr-van Leeuwen Theorem

• Basic Forms of Magnetic Media

  • Dia-, Para-, Superpara-, Ferro-, Antiferro-, Ferrimagnetism

  • Magnetic Susceptibility (Curie and Curie-Weiss-Law)

  • Weiss Molecular Field Theory

• Magnetic Interactions and Anisotropies
  • Heisenberg Exchange Interaction
  • Crystal Fields
  • Spin-Orbit Interaction (magnetocrystalline anisotropy)
  • Magnetostriction
  • Dipole-Dipole Interaction (shape anisotropy)
• Theory of Micromagnetism
  • Magnetic Energies (continuum approximation)
  • Magnetic Domains (Bloch and Néel walls)
  • Stoner-Wohlfarth Model
  • Magnons (Bloch T3/2-Law)
• Observational Techniques (can be moved to the Experimental techniques lecture)
  • Neutron Scattering
  • Kerr Microscopy
  • Lorentz Microscopy
  • Spin-Polarized Scanning Tunneling Microscopy
  • Magnetic Force Microscopy
  • Mössbauer Spectroscopy
• Magnetic Materials
  • Hard and Soft Magnets
  • Nanoparticles
  • Ferrofluids
  • Nanowires
  • Thin Films and Multilayers
• Basics of Superconductivity
  • Experimental Survey
  • Occurrence of Superconductivity
  • Destruction of Superconductivity of Magnetic Fields
  • Meissner Effect
  • Heat Capacity
  • Energy Gap
  • Microwave and Infrared Properties
  • Isotope Effect
  • Theoretical Survey
  • Thermodynamics of the Superconducting Transition
  • London Equation
  • Coherence Length
  • BCS Theory of Superconductivity
  • BCS Ground State
  • Flux Quantization in a Superconducting Ring
  • Duration of Persistent Currents
  • Type II Superconductors
  • Vortex State
  • Estimation of Hc1 and Hc2
  • Single Particle Tunneling
  • Josephson Superconductor Tunneling
  • DC Josephson Effect
  • AC Josephson Effect
  • Macroscopic Quantum Interference
  • High-Temperature Superconductors

1st take:

Oral exam (duration: about 30 minutes) at the end of the semester during the exam period (usual 20 point grade system, no additional material allowed).

Ìý

Rertake:

Oral exam (about 30 minutes), as before

Support: PowerPoint presentation
[1] C. Kittel, Physical Theory of Ferromagnetic Domains, Reviews of Modern Physics 21, 541 (1949).
[2] S. Blundell, Magnetism in Condensed Matter (Oxford ³ÉÈËÍ·Ìõversity Press, 2001).
[3] A. Aharoni, Introduction to the Theory of Ferromagnetism (Oxford ³ÉÈËÍ·Ìõversity Press, 2000).
[4] S. Chikazumi, Physics of Ferromagnetism, 2nd ed. (Oxford ³ÉÈËÍ·Ìõversity Press, 1997).
[5] A. Hubert and R. Schäfer, Magnetic Domains (Springer, Berlin, 1998).
[6] B.D. Cullity and C.D. Graham, Introduction to Magnetic Materials, 2nd ed. (Wiley, 2009).
[7] S. J. Blundell, Superconductivity (Oxford ³ÉÈËÍ·Ìõversity Press, 2009).

• Course title: Introduction to quantum technologyÌý
• Number of ECTS: 6
• Course code: MCMP-59
• Module(s): Quantum science and technology I
• Language: FR
• Mandatory: No

Provide a solid introduction to quantum technology, giving students the necessary understanding to navigate around and discuss about recent topics in the field.

Learning outcomes:
1.)Ability to understand the basic working principles of quantum systems (atoms, ions, quantum dots, and colour centres).
2.)Ability to understand how experiments use single-photon sources for fundamental physics experiments (interference, entanglement).
3.)Ability to understand how quantum systems are used for applications (communication, sensing).
4.)You will also obtain a basic introduction to quantum computing, covering the fundamentals of superconducting qubits, as well as how basic quantum algorithms work.

Week-1: Quantum physics: Introduction and historic overview.
Week-2: Working principles of basic quantum systems (e.g., single-photon sources based on quantum dots or colour centres).
Week-3: Understanding and using single-photon sources (Hanbury Brown and Twiss effect, secure quantum communication).
Week-4: Introduction to other similar quantum systems (atoms, ions).
Week-5: Generation of multi-photon quantum states (spontaneous parametric down-conversion, Hong-Ou-Mandel interference).
Week-6: Entanglement and Bell inequality.
Week-7: Bell inequality without loopholes (theory and experiments).
Weeks-8-9: Basics of quantum communication and quantum internet.
Weeks-10-11: Basics of quantum sensing.
Weeks-12-13: Basics of quantum computing.
Week-14: Basics of quantum algorithms.

Will be an oral exam (~45 minutes).

Mark Fox: Quantum Optics. An Introduction
Hans-A. Bachor and Thimothy C. Ralph: A Guide to Experiments in Quantum Optics
Jannik Hellenkamp and Dominique Unruh: Introduction to Quantum Computing
Christopher C. Gerry and Peter L. Knight: Introductory Quantum Optics
Michael A. Nielsen and Isaac L. Chuang: Quantum Computing and Quantum Information
John Preskill: Lecture Notes for Physics 209: Quantum Information and Computing
Scott Aaronson: Introduction to Quantum Information Science Lecture Notes
George Greenstein and Arthur G. Zajonc: The Quantum Challenge: Modern Research on the Foundations of Quantum Mechanics
Henry Semenenko et al.: Quantum Communication 101
Amnon Yariv: Quantum Electronics

• Course title: Non-linear dynamics and pattern formationÌý
• Number of ECTS: 6
• Course code: MCMP-60
• Module(s): Soft and living matter I
• Language: EN
• Mandatory: No

The objective of this course is to introduce the fundamental concepts and methods of nonlinear science, with emphasis on dynamical systems, stability, bifurcations, pattern formation, and chaos. The course aims to give students both a conceptual understanding of nonlinear phenomena and the analytical tools needed to study them in physical, chemical, and biological systems.

After completing this course, students will be able to:

• Identify and describe characteristic nonlinear phenomena arising in physics, chemistry, and biology.


• Formulate nonlinear dynamical models in a quantitative way and interpret their physical meaning.
  


• Analyze dynamical systems with a finite number of degrees of freedom, including fixed points and their local behavior.
  


• Apply linear stability analysis to determine the stability of stationary states.
  


• Perform a basic bifurcation analysis to characterize nonlinear behavior near fixed points.
  


• Explain how broken symmetries and spatially distributed interactions lead to pattern formation.
  


• Describe the main mechanisms leading to chaotic dynamics and distinguish regular from chaotic motion.

This course presents an introduction to nonlinear dynamics and pattern formation based on representative examples from the physical sciences and biology. Starting from the quantitative formulation of nonlinear models, the course develops the analysis of systems with a finite number of degrees of freedom, including fixed points, linear stability, and bifurcations. It then extends these ideas to spatially distributed systems, where symmetry breaking and nonlinear interactions generate patterns. The course concludes with an introduction to chaotic dynamics. The overall goal is to provide students with a coherent framework for understanding how complex temporal and spatial behavior emerges from simple nonlinear laws.

First Take:

The exam may be written or oral depending on the number of students. It will cover both CM and TD and will constitute 100% of the final grade.

Assessment rules:Ìý During exams only the lecture notes are allowed.Ìý

Assessment criteria: The assessment will evaluate the student’s understanding of the main concepts and methods developed in the course, including model formulation, stability analysis, bifurcation analysis, pattern formation, and chaotic dynamics. Students will be graded on the correctness of their reasoning, the clarity of their derivations and explanations, and their ability to apply the course tools to representative examples.

Re take : No retakes

Main references:
Introduction to nonlinear science by Grégoire Nicolis

• Course title: Physics of AIÌý
• Number of ECTS: 6
• Course code: MCMP-61
• Module(s): Machine learning for physics I
• Language: EN
• Mandatory: No

The objective of this course is to introduce the basic principles underlying modern artificial intelligence from a physics perspective. The course presents neural networks and learning algorithms as dynamical systems and probabilistic models, emphasizing connections with statistical mechanics, optimization, and stochastic processes.

After completing this course, students will be able to:
– Explain the fundamental principles behind neural networks and machine learning algorithms.
– Formulate learning problems in terms of optimization and probabilistic inference.
– Understand the role of stochastic dynamics and gradient-based learning in training neural networks.
– Analyze simple neural network models such as Hopfield networks and perceptrons.
– Describe the basic ideas behind deep learning, convolutional networks, and recurrent networks.

This course provides an introduction to machine learning with an emphasis on the physical and mathematical principles that underlie learning systems. Starting from Hopfield networks and perceptrons, the course develops the key ideas of supervised, unsupervised, and reinforcement learning. Throughout the course, learning algorithms are interpreted using concepts familiar from physics, such as energy landscapes, stochastic dynamics, and optimization. The goal is to provide students with both conceptual understanding and practical insight into how modern AI systems learn from data.

First Take:
The exam may be written or oral depending on the number of students. It will cover both CM and TD and will constitute 100% of the final grade.
No retake

Main reference:
Machine Learning with Neural Networks: An Introduction for Scientists and Engineers by Bernhard Mehlig
Additional references:
Information Theory, Inference and Learning Algorithms by David MacKay
The Statistical Mechanics Theory of Learning by A. Engel C. Van den Broeck.Ìý

• Course title: Introduction to quantum technologyÌý
• Number of ECTS: 6
• Course code: MCMP-59
• Module(s): Electives I
• Language: FR
• Mandatory: No

Provide a solid introduction to quantum technology, giving students the necessary understanding to navigate around and discuss about recent topics in the field.

Learning outcomes:
1.)Ability to understand the basic working principles of quantum systems (atoms, ions, quantum dots, and colour centres).
2.)Ability to understand how experiments use single-photon sources for fundamental physics experiments (interference, entanglement).
3.)Ability to understand how quantum systems are used for applications (communication, sensing).
4.)You will also obtain a basic introduction to quantum computing, covering the fundamentals of superconducting qubits, as well as how basic quantum algorithms work.

Week-1: Quantum physics: Introduction and historic overview.
Week-2: Working principles of basic quantum systems (e.g., single-photon sources based on quantum dots or colour centres).
Week-3: Understanding and using single-photon sources (Hanbury Brown and Twiss effect, secure quantum communication).
Week-4: Introduction to other similar quantum systems (atoms, ions).
Week-5: Generation of multi-photon quantum states (spontaneous parametric down-conversion, Hong-Ou-Mandel interference).
Week-6: Entanglement and Bell inequality.
Week-7: Bell inequality without loopholes (theory and experiments).
Weeks-8-9: Basics of quantum communication and quantum internet.
Weeks-10-11: Basics of quantum sensing.
Weeks-12-13: Basics of quantum computing.
Week-14: Basics of quantum algorithms.

Will be an oral exam (~45 minutes).

Mark Fox: Quantum Optics. An Introduction
Hans-A. Bachor and Thimothy C. Ralph: A Guide to Experiments in Quantum Optics
Jannik Hellenkamp and Dominique Unruh: Introduction to Quantum Computing
Christopher C. Gerry and Peter L. Knight: Introductory Quantum Optics
Michael A. Nielsen and Isaac L. Chuang: Quantum Computing and Quantum Information
John Preskill: Lecture Notes for Physics 209: Quantum Information and Computing
Scott Aaronson: Introduction to Quantum Information Science Lecture Notes
George Greenstein and Arthur G. Zajonc: The Quantum Challenge: Modern Research on the Foundations of Quantum Mechanics
Henry Semenenko et al.: Quantum Communication 101
Amnon Yariv: Quantum Electronics

• Course title: Physics of AIÌý
• Number of ECTS: 6
• Course code: MCMP-61
• Module(s): Electives I
• Language: EN
• Mandatory: No

The objective of this course is to introduce the basic principles underlying modern artificial intelligence from a physics perspective. The course presents neural networks and learning algorithms as dynamical systems and probabilistic models, emphasizing connections with statistical mechanics, optimization, and stochastic processes.

After completing this course, students will be able to:
– Explain the fundamental principles behind neural networks and machine learning algorithms.
– Formulate learning problems in terms of optimization and probabilistic inference.
– Understand the role of stochastic dynamics and gradient-based learning in training neural networks.
– Analyze simple neural network models such as Hopfield networks and perceptrons.
– Describe the basic ideas behind deep learning, convolutional networks, and recurrent networks.

This course provides an introduction to machine learning with an emphasis on the physical and mathematical principles that underlie learning systems. Starting from Hopfield networks and perceptrons, the course develops the key ideas of supervised, unsupervised, and reinforcement learning. Throughout the course, learning algorithms are interpreted using concepts familiar from physics, such as energy landscapes, stochastic dynamics, and optimization. The goal is to provide students with both conceptual understanding and practical insight into how modern AI systems learn from data.

First Take:
The exam may be written or oral depending on the number of students. It will cover both CM and TD and will constitute 100% of the final grade.
No retake

Main reference:
Machine Learning with Neural Networks: An Introduction for Scientists and Engineers by Bernhard Mehlig
Additional references:
Information Theory, Inference and Learning Algorithms by David MacKay
The Statistical Mechanics Theory of Learning by A. Engel C. Van den Broeck.Ìý

• Course title: Solid state spectroscopyÌý
• Number of ECTS: 6
• Course code: MCMP-63
• Module(s): Electives I
• Language: EN
• Mandatory: No

The objective of the course is to introduce students to vibrational spectroscopies in solids, explain their importance in solid state physics and describe their experimental aspects.

The student who passes this course will be able:
– to describe lattice vibrations and their importance
– to compute phonons using simple modelsÌý
– to identify the experimental methods to measure them

Lattice dynamics:
Group theory and symmetry
Computations and models
Experimental techniques:
Infrared spectroscopy
Inelastic scattering of light: Raman and Brillouin spectroscopies
Inelastic X-ray scattering
Inelastic neutron scattering

First Take:
30 minutes oral exam, no document allowed.
Re- Take:
Identical to the first exam.

PowerPoint presentation and other supporting material provided by the teacher.

• Course title: Electrochemical energy storageÌýsystems
• Number of ECTS: 3
• Course code: MCMP-62
• Module(s): Electives I
• Language: EN
• Mandatory: No

This course aims to understand the electro(chemical) working principles behind batteries and power to X fuel applications. The working principles will be examined in terms of electrochemical equations, the transport of matter and energetics. At the end of the course the students should be able to discuss the advantages and disadvantages of various systems in a quantitative way.

A student who passes this course will be able to:
– for electrochemical batteries explain:
The working principle in terms of electrochemical equations Calculate a maximum expected output voltage
Understand the energy losses present
Calculate the mass and volume energy density
Quantitatively explain the advantages and disadvantages of different systems.
Understand the physical limitationsÌý
– for hydrogen production explain:
Quantitatively the working principles and understand energy losses in the process.
Have some regard for the storage and transport of hydrogen
-for power to X or e-fuels explain:
Quantitatively assess the energetics of synthesizing the fuel.
Quantitatively compare and contrast the differences between combusting or using fuels cells to transform the energy stored inside the fuels.
Overall, the course will enable the student to critically study the literature on current research topics in the field of electrochemical and power to X storage systems.

Electrochemical energy storage covers the topics of batteries, flow-batteries, hydrogen production and the use of that hydrogen to make e-fuels commonly known as power to X. This course aims to understand the electro(chemical) working principles behind these applications. To do this we cover the following topics
Refresher on chemical thermodynamics (enthalpy, entropy, free energy, equilibrium constant, electrochemical potentials)
Electrochemistry (Nernst, over potential, rate constants, transfer co-efficient, electrical double layers, mass transport, voltammetry, chronopotentiometry)
Battery systems (Ideal, Daniels, lead acid, Li and Na ion, redox flow)
Hydrogen production (electrolysis, introduction to catalysis)
Fuel formation (reactions, energy density)
Fuel consumption (combustion vs fuel cells)

First Take:
First take : Oral exam
Assessment rules: Students will be assessed on the quality of their oral explanations as well as any diagrams or equations that they may write on the board or on paper. No electronic devices or notes will be allowed. Students will be asked questions verbally. They may also be presented with a picture / image or diagram which they will have to explain.
Assessment criteria: The oral exam is graded out of 20.

Retake: Same rules as above

All necessary learning material will be provided in pdf electronic format.

• Course title: Physics didactics 1
• Number of ECTS: 3
• Course code: BA_PHYS_GEN-36
• Module(s): Electives I
• Language: FR, DE, EN
• Mandatory: No

• Discover the richness of teaching physics


• Plan and experience teaching in front of a classÌý plan demo experiments


• analyse own performance to appreaciate challenges posed by teachingÌý ( insight intoÌý considered future plans into teaching carrers )


• presentation and discussion of different teaching methods

Learn about the challenges posed by teaching as such then teaching physics then in multilingual and academic environments, communicating, use of new techniques.Ìý

Students will get the opportunity to teach in a ‘real life’ situation in a secondary school class. Furthermore there are courses on how to prepare, student pre – and misconceptions, evaluative and formative assessment, practical work and latest multi-media methods e.g. Chat GPT, Fermi questions use, online teaching pros and cons, use of news in press, fake news,… The content varies slightly depending on calendar organization and class availabilities as well as time needed to prepare the lessons.

Assessment is done by handing in a portfolio at the end of the semester. This portfolio documents the different course topics, activities, lesson plans, teaching performance, …
Assessment by portfolio. Elements evaluated: regular attendance, participation, assignments, preparation, execution and analysis of practical part, set homework
Graded to 20 marks.

Assessment rules: portfolio has to be handed in by a deadline announced to the studentsÌý
Assessment criteria:
Practical part +/-50 %
Courses, assignments, participation +/- 50%
No Retake

Students are encouraged to take notes themselves. There is no course text handout. PPTs presented are sent to the students as well as any documents suitable.
G. de Vecchi, L’enseignement scientifique, Delagrave, 2002, ISBN: 2-206-08471-6
H. Gudjons, Handlungsorientiert lehren und lernen, Klinkhardt, 2008, 2008, ISBN: 978-3-7815-1625-0
Kirchner Girwidz Häußler, Physikdidaktik, Springer, 2001, ISBN: 3-540-41936-5
H. Klippert, Methodentraining, Beltz 2005, ISBN: 3-407-62545-6
A.B. Arons Teaching Introductory Physics, Wiley, 1996, ISBN: 978-04711-37078
M. Reiss Understanding Science Lessons, Open ³ÉÈËÍ·Ìõversity Press, 2001, ISBN: 978-0335-197699
H.K. Mikalsis (Hrsg.) Physik Didaktik, Cornelsen Scriptor, 2006, ISBN: 378-3589221486
Edited by J.Osborne and J. Dilon Good Practice in Science teaching, OUP 2010 ISBN: 978-033523858-3
Science learning and teaching, Routledge 3rd ed. , J. Wellington and G. Ireson 4th editionÌý
Journals:Ìý Physik in unserer Zeit, Praxis der Naturwissenschaften, The Physics Teacher, American Journal of Physics,..Ìý

• Course title: Magnetism and superconductivityÌý
• Number of ECTS: 6
• Course code: MCMP-58
• Module(s): Electives I
• Language: EN
• Mandatory: No

The lecture aims at

• introducing the student to the basic principles of magnetism, magnetic materials, and superconductivity


• applying these principles to problems in fundamental and applied magnetism and superconductivity


• developing the ability to critically assess certain issues in the field

After completion of the course, the student is expected to understand and explain

• the basic quantities in magnetism and superconductivity (magnetic moments, magnetic fields, magnetization, units, etc.)the basic phenomenology and the various manifestations of magnetism (hysteresis loop, domains and domain walls, spontaneous magnetization, Dia-, Para-, Superpara, Ferro-, Antiferro-, Ferrimagnetism, etc.)


• the basic magnetic interactions and anisotropies (exchange interaction, magnetocrystalline anisotropy, magnetostriction, magnetodipolar interaction, Zeeman interaction, etc.)


• simple models for magnetism (Mean-Field theory, Curie-Weiss law, Bloch law (magnons), Stoner-Wohlfarth model, micromagnetism, etc.)


• the basics of the main experimental techniques (ac+dc magnetization, neutron scattering, spin-polarized STM, Kerr microscopy, Lorentz microscopy, magnetic force microscopy, Mössbauer spectrocopy, etc.)


• the basics of magnetic materials (hard and soft magnets, ferrofluids, nanoparticles, thin films and multilayer structures, etc.)


• to understand and explain the basic phenomenology of superconductivity

Syllabus

• Introduction
  • Basic Quantities (magnetic moment, orbital and spin angular momentum, Hund rules, …)
  • ³ÉÈËÍ·Ìõts, Fields, …
  • Bohr-van Leeuwen Theorem

• Basic Forms of Magnetic Media

  • Dia-, Para-, Superpara-, Ferro-, Antiferro-, Ferrimagnetism

  • Magnetic Susceptibility (Curie and Curie-Weiss-Law)

  • Weiss Molecular Field Theory

• Magnetic Interactions and Anisotropies
  • Heisenberg Exchange Interaction
  • Crystal Fields
  • Spin-Orbit Interaction (magnetocrystalline anisotropy)
  • Magnetostriction
  • Dipole-Dipole Interaction (shape anisotropy)
• Theory of Micromagnetism
  • Magnetic Energies (continuum approximation)
  • Magnetic Domains (Bloch and Néel walls)
  • Stoner-Wohlfarth Model
  • Magnons (Bloch T3/2-Law)
• Observational Techniques (can be moved to the Experimental techniques lecture)
  • Neutron Scattering
  • Kerr Microscopy
  • Lorentz Microscopy
  • Spin-Polarized Scanning Tunneling Microscopy
  • Magnetic Force Microscopy
  • Mössbauer Spectroscopy
• Magnetic Materials
  • Hard and Soft Magnets
  • Nanoparticles
  • Ferrofluids
  • Nanowires
  • Thin Films and Multilayers
• Basics of Superconductivity
  • Experimental Survey
  • Occurrence of Superconductivity
  • Destruction of Superconductivity of Magnetic Fields
  • Meissner Effect
  • Heat Capacity
  • Energy Gap
  • Microwave and Infrared Properties
  • Isotope Effect
  • Theoretical Survey
  • Thermodynamics of the Superconducting Transition
  • London Equation
  • Coherence Length
  • BCS Theory of Superconductivity
  • BCS Ground State
  • Flux Quantization in a Superconducting Ring
  • Duration of Persistent Currents
  • Type II Superconductors
  • Vortex State
  • Estimation of Hc1 and Hc2
  • Single Particle Tunneling
  • Josephson Superconductor Tunneling
  • DC Josephson Effect
  • AC Josephson Effect
  • Macroscopic Quantum Interference
  • High-Temperature Superconductors

1st take:

Oral exam (duration: about 30 minutes) at the end of the semester during the exam period (usual 20 point grade system, no additional material allowed).

Ìý

Rertake:

Oral exam (about 30 minutes), as before

Support: PowerPoint presentation
[1] C. Kittel, Physical Theory of Ferromagnetic Domains, Reviews of Modern Physics 21, 541 (1949).
[2] S. Blundell, Magnetism in Condensed Matter (Oxford ³ÉÈËÍ·Ìõversity Press, 2001).
[3] A. Aharoni, Introduction to the Theory of Ferromagnetism (Oxford ³ÉÈËÍ·Ìõversity Press, 2000).
[4] S. Chikazumi, Physics of Ferromagnetism, 2nd ed. (Oxford ³ÉÈËÍ·Ìõversity Press, 1997).
[5] A. Hubert and R. Schäfer, Magnetic Domains (Springer, Berlin, 1998).
[6] B.D. Cullity and C.D. Graham, Introduction to Magnetic Materials, 2nd ed. (Wiley, 2009).
[7] S. J. Blundell, Superconductivity (Oxford ³ÉÈËÍ·Ìõversity Press, 2009).

• Course title: Non-linear dynamics and pattern formationÌý
• Number of ECTS: 6
• Course code: MCMP-60
• Module(s): Electives I
• Language: EN
• Mandatory: No

The objective of this course is to introduce the fundamental concepts and methods of nonlinear science, with emphasis on dynamical systems, stability, bifurcations, pattern formation, and chaos. The course aims to give students both a conceptual understanding of nonlinear phenomena and the analytical tools needed to study them in physical, chemical, and biological systems.

After completing this course, students will be able to:

• Identify and describe characteristic nonlinear phenomena arising in physics, chemistry, and biology.


• Formulate nonlinear dynamical models in a quantitative way and interpret their physical meaning.
  


• Analyze dynamical systems with a finite number of degrees of freedom, including fixed points and their local behavior.
  


• Apply linear stability analysis to determine the stability of stationary states.
  


• Perform a basic bifurcation analysis to characterize nonlinear behavior near fixed points.
  


• Explain how broken symmetries and spatially distributed interactions lead to pattern formation.
  


• Describe the main mechanisms leading to chaotic dynamics and distinguish regular from chaotic motion.

This course presents an introduction to nonlinear dynamics and pattern formation based on representative examples from the physical sciences and biology. Starting from the quantitative formulation of nonlinear models, the course develops the analysis of systems with a finite number of degrees of freedom, including fixed points, linear stability, and bifurcations. It then extends these ideas to spatially distributed systems, where symmetry breaking and nonlinear interactions generate patterns. The course concludes with an introduction to chaotic dynamics. The overall goal is to provide students with a coherent framework for understanding how complex temporal and spatial behavior emerges from simple nonlinear laws.

First Take:

The exam may be written or oral depending on the number of students. It will cover both CM and TD and will constitute 100% of the final grade.

Assessment rules:Ìý During exams only the lecture notes are allowed.Ìý

Assessment criteria: The assessment will evaluate the student’s understanding of the main concepts and methods developed in the course, including model formulation, stability analysis, bifurcation analysis, pattern formation, and chaotic dynamics. Students will be graded on the correctness of their reasoning, the clarity of their derivations and explanations, and their ability to apply the course tools to representative examples.

Re take : No retakes

Main references:
Introduction to nonlinear science by Grégoire Nicolis

• Course title: Semiconductors and Solar Cells
• Number of ECTS: 4
• Course code: MCMP-33
• Module(s): Module 2.4
• Language: EN
• Mandatory: Yes

This course aims at:
giving a short overview/repetition on the electronic structure of semiconductors (bands and defects)
introducing the students to charge carrier statistics
educating the students on the optical properties of semiconductors
training the students in basics of pn junctions
introducing the students to junctions under illumination and the functioning of solar cells
introducing the students to the thermodynamic balances in solar cells

A student who passes this course will be able to:
– understand the role of doping in semiconductors
– describe qualitatively and quantitatively absorption and light emission in semiconductors
– delineate qualitatively and quantitatively the behaviour of pn junctions in the dark and under illumination
– explain qualitatively and quantitatively the efficiency limits in solar cells
The course will enable the student to study the literature on current research topics in the field of semiconductor physics.

Electronic structure of semiconductors
Charge carrier statistics
Excitation and recombination
The equilibria in a solar cellsÌý
p/n junction in the dark and under illumination

Task 1: active and successful participation in TD
Assessment rules: submit homework via moodle
Assessment criteria: regular participation and at least 2/3 of the problems attempted – are prerequisite to participate in oral exam
Task 2: Written mid term exam
Assessment rules: first part no resources besides the students’ brain, second part: open book (any paper resources allowed), no electronic devices
Assessment criteria: weight on final grade 1/3
At least 6/20 point prerequisite to take oral exam
Task 2: Oral exam in exam period
Assessment rules: 30min oral exam
Assessment criteria: weight on final grade 2/3

Support :
Lecture Slides
Ìý
Literature :
– R. F. Pierret, Advanced Semiconductor Fundamentals, Prentice Hall
– P. Yu and M. Cardona, Fundamentals of Semiconductors: Physics and Materials Properties, Springer
– K. Seeger, Semiconductor Physics, Springer
– S.M. Sze, K.K. Ng, Physics of Semiconductor Devices, Wiley
– P. Würfel, Physics of Solar Cells, Wiley
– M. Grundmann, The Physics of Semiconductors, Springer
– J. Pankove, Optical Processes in Semiconductors, Dover
РW. M̦nch, Electronic Properties of Semiconductor Interfaces, Springer
Ìý

• Course title: Nonequilibrium soft and active matter
• Number of ECTS: 4
• Course code: MCMP-47
• Module(s): Module 2.5
• Language: EN
• Mandatory: Yes

Students will be given an overview of the techniques required to model and analyze fluctuations for a large class of systems in soft and living matter. First, we will present the equivalence between Langevin equation, Fokker-Planck equation, and path probability to describe the time-evolution of a stochastic process. On this basis, we will establish the essential properties of equilibrium, including steady-state properties (Boltzmann distribution, equipartition theorem) and relaxation to steady state (linear response, fluctuation-dissipation theorem, Green-Kubo formulas). We will also discuss how the laws of thermodynamics extend to stochastic processes (stochastic thermodynamics, fluctuation theorems), with applications to colloidal engines. Then, we will introduce a specific class of nonequilibrium systems, which extract energy from their environment to sustain an individual directed motion, known as *active matter*. We will discuss the consequences of self-propulsion in specific examples. For many-body systems, we will show that it can lead to collective effects without any equilibrium equivalent, which will be rationalized based on coarse-grained hydrodynamic equations.

Students will become familiar with techniques of statistical mechanics to analyze fluctuations beyond steady state, both for equilibrium and nonequilibrium systems, including recent progress in stochastic thermodynamics.

> Modeling fluctuations: Langevin equation, Fokker-Planck equation, path probability
> Symmetry of fluctuations: fluctuation-dissipation, linear response, fluctuation theorems
> Stochastic thermodynamics: energetics at microscopic scale, first and second laws, engines
> Active matter: particle-based approach, collective effects, consequences of irreversibility
> Field theories: coarse-graining microscopic dynamics, extended Landau-Ginzburg approach

Oral and/or written exam.

Relevant literature
– Van Kampen, ‘Stochastic processes in physics and chemistry’
– Gardiner, ‘Handbook of stochastic methods’
– Risken, ‘The Fokker-Planck equation’
– Chaikin, Lubensky, ‘Principles of condensed matter physics’
– Chandler, ‘Introduction to modern statistical mechanics’.

• Course title: Advanced experimental and theoretical laboratory classes (Part 2)
• Number of ECTS: 3
• Course code: MCMP-13
• Module(s): Module 2.6
• Language:
• Mandatory: Yes

The module aims at

• 
  familiarizing the student with modern research topics in experimental and theoretical condensed-matter physics
  


• 
  fostering the student’s ability to autonomously achieve scientific tasks
  


• 
  introducing the student to modern experimental techniques and challenging theoretical approaches
  


• 
  strengthening the student’s experimental and analytic skills
  


• 
  developing the student’s capability to interpret and properly describe scientific results
  

A student who passes this course is expected to be able

• to tackle new scientific tasks in experimental and theoretical condensed-matter physics


• to familiarize himself with modern experimental tools and challenging theoretical approaches


• to work on a modern research topic with a proper autonomy


• to work out and defend scientific reports

• Scanning Tunneling Microscopy and Spectroscopy (16 hours)
• Scanning electron microscopy with X-ray microanalysis (16 hours)
• X-ray diffraction (8 hours)
• Quantitative Microscale Imaging in Biological Physics (16 hours)
• One dimensional quantum systems (16 hours)
• Rheology (16 hours)
• Scanning Tunneling Microscopy and Spectroscopy (16 hours)
• Scanning electron microscopy with X-ray microanalysis (16 hours)
• X-ray diffraction (8 hours)
• Quantitative Microscale Imaging in Biological Physics (16 hours)
• One dimensional quantum systems (16 hours)
• Rheology (16 hours)

Written reports on experiments; continuous control

Support :
Handouts describing topics and tasks and literature references indicated therein
Literature :
Handouts describing topics and tasks and literature references indicated therein

• Course title: Literature Seminar
• Number of ECTS: 2
• Course code: MCMP-7
• Module(s): Module 2.7
• Language: EN
• Mandatory: Yes

The course aims at introducing the student to basic topics of condensed-matter physics as well as teaching him/her to read scientific literature, and to present and defend its contents in an oral presentation.

A student who passes this course will be able to:
read and understand the main ideas of a scientific article
present and defend a piece of scientific research to an audience

Seminar topics are proposed by the professors from the Departement of Physics and Materials Science along with a corresponding corpus of scientific literature. The seminar is prepared during one-to-one appointments between students and teachers and delivered in front of the group at the end of the semester. Examples of past topics include – for illustration purposes only:Ìý

Solution of the 1D Ising model
Capillary waves at gas liquid interfaces
Skyrmion lattices in metallic and semiconducting B20 transition metal compounds
Dynamic nuclear polarization
Graphene and other two-dimensional materials: physical properties and potential technological applications.
Topological insulators: what are they, how do they work, and what is their technological relevance?

Oral presentation (seminar)

Support / Literature: Scientific articles and references therein as provided by the teachers.

• Course title: Didactics for Physics 2
• Number of ECTS: 3
• Course code: BA_PHYS_GEN-26
• Module(s): Module Options 2.8
• Language: EN
• Mandatory: No

• découvrir la richesse de l’enseignement de la physique


• planifier et vivre des situations de TP en classe


• expérimenter différentes méthodes modernes d’enseignement


• analyser ses propres performances pour mieux s’orienter dans son choix professionnel


• évaluer la performance des élèves


• comprendre l’enseignement de la physique au secondaire et secondaire technique

Connaître les multiples facettes de l’apprentissage et de l’enseignement de la physique et les défis posés à l’enseignant.

Engagement régulier, élaboration d’un portfolio personnel (pièces créées à partir des éléments traités en cours), présentation du portfolio

Notes de cours
G. de Vecchi, L’enseignement scientifique, Delagrave, 2002, ISBN: 2-206-08471-6
H. Gudjons, Handlungsorientiert lehren und lernen, Klinkhardt, 2008, 2008, ISBN: 978-3-7815-1625-0
Kirchner Girwidz Häußler, Physikdidaktik, Springer, 2001, ISBN: 3-540-41936-5
H. Klippert, Methodentraining, Beltz 2005, ISBN: 3-407-62545-6
A.B. Arons Teaching Introductory Physics, Wiley, 1996, ISBN: 978-04711-37078
M. Reiss Understanding Science Lessons, Open ³ÉÈËÍ·Ìõversity Press, 2001, ISBN: 978-0335-197699
H.K. Mikalsis (Hrsg.) Physik Didaktik, Cornelsen Scriptor, 2006, ISBN: 378-3589221486

• Course title: Partial Differential Equations II
• Number of ECTS: 8
• Course code: MAMATH-156
• Module(s): Module Options 2.8
• Language: EN
• Mandatory: No

Learning tools in order to deal with PDE, understanding the interplay between local and global problems and techniques.

Distributions as generalized functions continued, Sobolev spaces, elliptic regularity,
elliptic operators on compact manifolds, some non-linear equations.

Written exam

Literatur

• Jost: Postmodern analysis


• Folland: Introduction to partial differential equations


• Reed-Simon: Methods of mathematical physics I-IV


• Aubin: Nonlinear analysis on manifolds

• Course title: Knowledge Discovery and Data Mining
• Number of ECTS: 5
• Course code: MICS2-13
• Module(s): Module Options 2.8
• Language: EN
• Mandatory: No

We understand Data Mining (Knowledge Discovery) as a life-cylce process fromÌý data to information and insights. In times of Big data, Data Mining has become a central interest both for industry and academia. In this course, we discuss several data-related aspects like preprocessing or pricacy as well as selected aspects of Machine Learning. An expansive definition of Data Mining, which is the derivation of insights from masses of data by studying and understanding the structure of the constituent data, and selected applications complete the course.

* Explain the fundamental concepts of data mining and knowledge discovery
* List the properties of data relevant for deriving interesting and useful information/observation from that.
* Explain machine learning algorithms and strategies to deploy the discovered results
* Argue the importance of domain knowledge during the data analysis with its scope and limitations

* Definition and Process.
* Data Mining, Data Science, and the Big Data Hype.
* Data Quality and Preprocessing
* Data Privacy and Security.
* Data and Information Visualization.
* Machine Learning for Clustering, Classification, Association Discovery, Sequential Pattern Analysis, and/or Time Series Analysis.

60% oral or written examination; 40% midterm tests

Selected references:

* M. Berry, G. Linoff: Mastering Data Mining, John Wiley Sons, 2000.
* U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy: Advances in Knowledge
Discovery and Data Mining, AAAI/MIT Press, 1996.
* J. Han, M. Kamber: Data Mining: Concepts and Techniques, 2nd edition, Morgan
Kaufmann, ISBN 1558609016, 2006.
* I. Witten, E. Frank, M. Hall: Data Mining: Practical Machine Learning Tools and
Techniques, 3nd Edition, Morgan Kaufmann, 2011.

• Course title: Communicating science
• Number of ECTS: 3
• Course code: MCMP-48
• Module(s): Module Options 2.8
• Language:
• Mandatory: No

Learn to simplify without loss of accuracy when dealing with non-experts
Know your audience
Learn how to deal with nervousnessÌý
Learn how to explain things simply
Improvement of presentation skills (interactive, body language, pace)

Presentation skills
Organizational skillsÌý
Teaching skills
Outreach skills

The course is a mix of theoretical introductions, practical experiences within the group and finally outreach activites in direct contact with the public (high school students, general public at events). All field work will be performed within the frame of the Scienteens Lab’s workshops. This requires some flexibility regarding the personal schedule.

Active participation
Attendance
Written final project, report, presentation or movie

Support :

The course material is dynamically evolving within the group and part of the course process. Exemplary material will be provided and the participants can bring examples themselves.
Literature :
Pierre Laszlo: Communicating Science, A Practical GuideÌý
Carmine Gallo: Talk Like TED
…and many more

• Course title: Master Thesis 1
• Number of ECTS: 30
• Course code: MCMP-50
• Module(s): Module 3.1
• Language:
• Mandatory: Yes

• Course title: Master Thesis 2
• Number of ECTS: 30
• Course code: MCMP-52
• Module(s): Module 4.1
• Language: EN
• Mandatory: Yes

The objective of the master thesis is to develop the student’s autonomy in the different dimensions of a research work.Ìý

By the end of the project, the student shall be able to know and report on the state of the art in the research field, master relevant research methods, produce original results and defend them in writing and orally.

Master thesis 2 is a continuation of the research project started in Master thesis 1.

Task 1: Master thesis
Assessment rules: The Master thesis is an original document written by the student. It has to undergo a plagiarism check. The length of the document is left to the appreciation of the supervisor(s) and may depend on the nature of the research work. The grade is agreed on by the supervisor and the co-evaluator.Ìý
Assessment criteria: The report is graded based on the clarify of the hypothesis, the quality and presentation of the scientific results, the compliance with the standards of scientific writing.Ìý
Task 2: Oral defense of the Master thesis
Assessment rules: The defense is held in front of the supervisor(s) and co-evaluator and is open to other members of the department. The defense consists in a 30-minute presentation followed by about 20 minutes of questions. All attending professors may participate in the deliberation.Ìý
Assessment criteria: In addition to the intrinsic scientific content evaluated in the written report, the defense is graded based on the quality of the oral presentation and the ability to answer scientific questions raised by the audience.Ìý
The final mark is the average of the two marks for the written thesis and the oral defense.Ìý

Depending on the research topic