����ͷ��

• Course title: BPHY Lab classes 2
• Number of ECTS: 4
• Course code: BA_PHYS_GEN-14
• Module(s): Module 3.3
• Language: EN
• Mandatory: Yes

The students get to know physical laws and relations by conducting experiments.

Consolidation of the knowledge gained in the theoretical undergraduate courses.
Getting skills in experimental physics.
Learning how to deal with experimental errors.
Learning how to write scientific reports.
Evaluate experimental data with the computer.

The students work in groups of two or maximum three persons during the experimental sessions. They conduct at least eight physical experiments from the following list:
Gyroscope
Speed of light
Mechanical Oscillator
Adiabatic coefficient
Van der Waals experiment
Surface tension
Hydrogen Atom
Interferometry
Millikan experiment
Dielectric properties
For each experiment, each group must write a report which is graded.
Twice the semester, the students must pass an oral exam.

Continuous evaluation:
The final mark consists of��
Evaluation of the conduction of the practical works during the sessions and evaluation of the reports for each experiment (33.3%)
Each of the 8 reports must be graded with at least 10/20 in order to pass the course. The students have the possibility to resubmit improved versions of reports if grading is too low.��
Evaluation of two oral exams during the semester (66.7%)
Retake exam is not possible (continuous evaluation).

If the student fail the course, he will have to register to the course the following year.

��

Support / Arbeitsunterlagen / Support:
Description of the experiments and additional information/literature made available on the courses’ Moodle page.

Littérature / Literatur / Literature:
Description of the experiments and additional information/literature made available on the courses Moodle page.

��

• Course title: Mathematical Methods 3
• Number of ECTS: 4
• Course code: BA_PHYS_GEN-15
• Module(s): Module 3.3
• Language: FR, EN
• Mandatory: Yes

Introduction to the basic concepts of the complex analysis with applications to Fourier transforms, series and differential equations. Introduction to the basic mathematical framework of the quantum theory.

Integration of simple meromorphic functions, Fourier transforms, diagonalization of endomorphisms, notion of scalar product, series expansion.

Complex analysis, Complex integration, Cauchy and residue Theorems.
Vector spaces in finite and infinite dimensions
Functional spaces, Hilbert spaces
Sturm-Liouville problem and orthogonal polynomials

Final exam: Written exam

Assessment rules: Student cannot use any notes nor electronic devices

Assessment criteria: Graded out of 20

��

Retake exam offered

written exam (same rules as the final exam)

Support / Literature
Serie Schaum Mathematique : Analyse complexe, Algèbre linéaire
Byron and Fuller : mathematics of classical and quantum physics��
Karevski : Physique quantique des champs et des transitions de phase.
Walter Appel : Mathematics for Physics and Physicists

• Course title: Probabilités et statistique appliquée pour ingénieurs et physiciens 1
• Number of ECTS: 5
• Course code: BA_PHYS_GEN-17
• Module(s): Module 3.3
• Language: EN
• Mandatory: Yes

Understand the concepts of randomness, probabilities and statistics.

A good handle of the concept of probability, uncertainty, descriptive statistics and discrete random variables, as well as know how to avoid the typical statistical mistakes.

The course will start with a motivation to know probability and statistics, in particular by showing its uses in everyday life and in particular in physics and engineering. We will then see descriptive statistics and how to avoid the typical statistical mistakes. Next we will lay out the basics of probability theory, (discrete) random variables, stochastic simulations, and finally draw a link to modern-day topics such as artificial intelligence and data science.

The evaluation is based on the written exams like midterm and final exam. The knowledge of theory and exercises is evaluated with a
mark over 20 points.
First Take:
First take : Written final exam
Assessment rules: The midterm concerns exercises as well as conceptual questions about the theory. The final exam
concerns exercises as well as conceptual questions about the theory.
Assessment criteria: The final mark will be equal to the maximum number between the Final Exam Grade (FEG) and
the sum of Final Exam Grade and Midterm Grade (MG), divided by two: FG=max(FEG, (FEG + MG)/2)

Re- Take:
The re-take exam concerns exercises as well as conceptual questions about the theory.
Re take : Exactly the same type of written exam like the final exam.
Assessment rules:
The re-take exam concerns theory and exercises
Assessment criteria:
The mark of the re-take exam is given over 20 points.

Slides that will be handed out before each course (except for the very first course).

• Course title: Experimental Physics 3 : Modern physics
• Number of ECTS: 6
• Course code: BA_PHYS_GEN-12
• Module(s): Module 3.1
• Language: EN
• Mandatory: Yes

The course on modern physics describes the new physics, that was developed in the first half of the 20th century. As an experimental course, we will emphasise the experimental evidence that triggered the development of modern physics. The course lays the foundations for the rigorous treatment of quantum mechanics in the 4th semester.
The course aims to clarify the fact, that physics is a science in evolution, where new observations may lead to completely new theories.

Students will��
-understand the challenges for classical physics and how they led to the development of the theory of special relativity and of quantum mechanics
-understand the basic laws and principles of special relativity and of quantum mechanics, in particular, where they are counter-intuitive with respect to everyday experience
-deal confidently with the laws and principles of basic atomic physics
-can apply these laws to unknown problems

1. Einstein’s trains and elevators – relativity��
2. Particles and waves – quantisation and uncertainty
3. An introduction to quantum mechanics – Schrödinger’s equation
4. Atomic physics – the periodic system of elements
5. A short introduction to molecular physics

Task 1: written midterm exam

��
Task 2: oral final exam
��
Assessment rules:
task 1:�� first part: no resources, second part: any paper resource allowed, no devices that can connect to the internet
At least 6/20 points from task1 are necessary to participate in task 2
task 2: QA, no detailed calculations or derivations
��
Assessment criteria:
task 1: 6/20 points is prerequisite for final exam
weight for final grade: 1/3
task 2: weight for final grade: 2/3
��
Retake exam – rules:
new oral exam.
final grade: 1/3 midterm of previous semester + 2/3 new oral exam

Copies of the slides available on Moodle
Books:
Paul Tipler, Ralph Llewellyn “Modern Physics” (in English and German)
Randy Harris “Modern Physics”
Stephen Thornton, Andrew Rex “Modern Physics for Scientists and Engineers”
“The Feynman lectures on physics” (in English, French and German)��
https://feynmanlectures.caltech.edu��
Harris Benson “Physique” 3 (in English and French)
Wolfgang Demtröder “Experimentalphysik” (in German)

• Course title: Theoretical Physics 2: Electrodynamics and Relativity
• Number of ECTS: 6
• Course code: BA_PHYS_GEN-13
• Module(s): Module 3.2
• Language: EN
• Mandatory: Yes

Understanding the concepts of a field theory; Acquiring the mathematical and theoretical skills to describe electro-magnetic phenomena starting from the Maxwell Equations.

Besides a profound overview over the classical theory of electro-magnetism, the student will acquire the necessary knowledge to treat electrodynamic phenomena within atomic, solid-state, soft-matter physics and other advanced branches of physics and material sciences. The mathematical skills acquired will also serve later for the solution of problems in quantum mechanics.

1.)�� �� ��Introduction to Electrostatics and Electrodynamics
2.)�� �� Maxwell Equations in Vacuum
3.)�� �� Boundary-Value Problems in Electrostatics
4.)�� �� Multipole Expansion
5.)�� �� Magnetostatics
6.)�� �� Electromagnetic waves, wave propagation, scattering, diffraction
7.)�� �� Electrodynamics in macroscopic media
8.)�� �� Special theory of relativity.

Midterm and final written and/or oral exam

Support / Literature:

D. Griffith, Introduction to Electrodynamics, Prentice-Hall (1991)
R.J. Jelitto, Theoretische Physik 3: Elektroydynamik, Aula-Verlag (1987)
J.D. Jackson, Classical Electrodynamics, Wiley Sons (1999)

• Course title: Analyse 3
• Number of ECTS: 7
• Course code: BA_MATH_GEN-17
• Module(s): Module Electives 3.4 (5 ECTS required to close the module)
• Language: FR, EN
• Mandatory: No

Les étudiants ayant suivi avec succès le cours d’analyse 3 seront capables de :

• Manipuler correctement les séries de fonctions et séries entières en particulier
• Appliquer les résultats classiques de la théorie des fonctions de plusieurs variables réelles
• Résoudre des problèmes d’application simples

Dans ce cours, on diversifie et approfondit diverses connaissances et techniques de l’analyse mathématique. On s’intéresse à démontrer plusieurs théorèmes fondamentaux dans l’étude des fonctions de plusieurs variables, des équations différentielles et des suites de fonctions.

Programme

• Fonctions implicites et applications
• Théorie locale des équations différentielles ordinaires
• Convergence de suites de fonctions
• Série de puissances
• L’exponentielle matricielle
• Théorème d’approximation de Stone-Weierstrass
  

Contrôle continu et examen écrit

Littérature��

• W. Rudin: Principes d’analyse mathématique.


• Des notes de cours sont mises à disposition des étudiants.

• Course title: Analyse 3b
• Number of ECTS: 5
• Course code: BA_PHYS_GEN-38
• Module(s): Module Electives 3.4 (5 ECTS required to close the module)
• Language: EN
• Mandatory: No

• To understand and use appropriately the mathematical language, identify the hypotheses and conclusions, as well as to develop and express rigorous arguments.


• To grasp new mathematical concepts building on previous ones (mainly from Analysis and Applications 1 and 2, as well as Linear Algebra).��


• To explain line, double and triple integrals and the relation between them


• To introduce elements of Functional Analysis, emphasizing the notion and significance of Hilbert spaces.


• To introduce Fourier series and Fourier transforms by studying the key results and examples.


• To present some interesting applications of Fourier analysis to the real world.��

��

• Learn the physical interpretation of line, double triple integrals and their relation via classical theorems (Fundamental Theorem of Calculus, Green and divergence)


• The student will understand the notion of Hilbert spaces and will learn the main examples and properties.


• The student will use bounded linear operators and learn the significance of the Riesz representation theorem.


• The student will be able to compute Fourier transforms and convolutions of functions.
  

1.�� Multiple Integrals and Classical Theorems in Vector Analysis
2.�� ELEMENTS OF HILBERT SPACES
Norms and distances. Bounded linear operators. Inner spaces. Hilbert spaces and main examples. Nice properties of Hilbert spaces: projections, Bessel inequality and orthonormality. Linear functionals. The Riesz representation theorem.
3.�� FOURIER SERIES
Convergence of functions: pointwise, uniform and L^2-convergence. Definition of Fourier series. Computation of the Fourier coefficients. Main properties.
��

Task 1: Written exams
Assessment rules:�� No electronic devices
Assessment criteria: 50% Midterms, 50% Final exam.

Retake exam offered – rules:�� Written exam 100%

Bibliography
-Basic references:
-Applied Analysis, John K. Hunter Bruno Nachtergaele, available at Nachtergaele’s webpage, 2000.
-Introductory Functional Analysis with Applications, Erwin Kreyszig, John Wiley Sons, 1978, ISBN: 978-0471504597.
-Elementary Classical Analysis, Jerrold E. Marsden Michael Hoffman, W. H Freeman, 1993, ISBN: 978-0716721055.��
-Analyse 3b pour le BASI physique et ingénierie, Jean-Marc Schlenker, 2014, available at Schlenker’s webpage.
-Partial Differential Equations: An Introduction, Walter A. Strauss, John Wiley Sons, 2008, ISBN: 978-0470-05456-7.��

-Complementary references:
-A Course in Functional Analysis, John B. Conway, Springer-Verlag New York, 2007, ISBN: 978-0-387-97245-9.��
-Fourier Analysis, Javier Duoandikoetxea, American Mathematical Society, 2001, ISBN: 978-0-8218-2172-5.
-Partial Differential Equations, Lawrence C. Evans, American Mathematical Society, 2010, ISBN: 978-0821849743.
-Real Analysis: Modern Techniques and their applications, Gerald B. Folland, John Wiley Sons, 2007, ISBN: 978-0471317166.
-Fourier series, Fourier transforms, and function spaces: a second course in Analysis, Tim Hsu, American Mathematical Society, 2020, ISBN: 978-1470451455.
-A student’s guide to Fourier transforms (with applications in Physics and Engineering), J. F. James, Cambridge ����ͷ��versity Press, 2011, ISBN: 978-052117683 5.��
-Introductory Functional Analysis with Applications, Erwin Kreyszig, John Wiley Sons, 1978, ISBN: 978-0471504597.��
-Lectures on the Fourier transform and its applications, Brad G. Osgood, American Mathematical Society, 2019, ISBN: 978-1470441913.��
-An introduction to partial differential equations, Yehuda Pinchover Jacob Rubinstein, Cambridge university Press, 2005, ISBN: 978-0521613231.
-Fourier analysis: an introduction, Elias M. Stein Rami Shakarchi, Princeton ����ͷ��versity Press, 2003, ISBN: 978-069111384-5.��

• Course title: Programming for Physics
• Number of ECTS: 4
• Course code: BA_PHYS_GEN-30
• Module(s): Module Electives 3.4 (5 ECTS required to close the module)
• Language: EN
• Mandatory: No

Efficiently implement common physics-related calculations using a computer, with an understanding of numerical constraints on accuracy and time.
Become familiar with famous computational models for physical processes showing chaotic or complex dynamics.

A student should be able to:��
–�� Write a python program…
�� …to describe the time-evolution of a dynamical system
�� …to solve a field equation on a grid
�� …to solve a multidimensional optimisation
–�� Describe the behaviour of complex or stochastic dynamical systems in a statistical way
–�� Present results graphically in each case.

Basic programming skills, with interactive python worksheets for plotting.

This course is an introduction to both computation for physics, and computational physics: a training in the computer skills needed to implement common physics calculations, and an introduction to the types of physical models which require computational treatment.

��

Task 1: Weekly electronic submission of completed interactive notebooks, including computer code and graphical presentation and discussion of self-generated data.�� Any student who feels that their continuous assessment is a poor reflection of their ability may request an exam.
Assessment rules: Collaboration between students is encouraged, cut-paste plagiarism is discouraged by forfeiting marks for all parties with overly similar work. Work which is directly copied�� from online or AI resources without understanding will be penalised also.
Retake exam offered

Rules: If a student wishes to retake the course, they may do so by repeating the process of continuous assessment or by requesting to sit an exam. Any student who feels that their continuous assessment marks are a poor reflection of their ability may request an exam. The exam will take place at a computer (disconnected from the internet) and will consist of a series of short and simple computational physics programming challenges.

Please note that only students who have already attended the course will be able to retake the exam, and those who have not attended will have to re-enrol the following year.

Written notes are provided as part of the interactive material. These notes do not form a complete repository of knowledge needed to pass the course.

• Course title: Topologie générale
• Number of ECTS: 5
• Course code: BA_MATH_GEN-20
• Module(s): Module Electives 3.4 (5 ECTS required to close the module)
• Language: FR, EN
• Mandatory: No

Au terme du cours l’étudiant doit être à même de

• maîtriser les concepts de base de la topologie générale et de la topologie des espaces métriques ainsi que les notions de connexité, de compacité et plus généralement d’invariants topologiques;


• appliquer les outils de la topologie générale pour résoudre des problèmes d’analyse ou d’algèbre.

Apprendre les fondements de la topologie générale, avec un accent sur la topologie métrique.

Programme

• Espaces métriques, boules, ouverts, topologie des espaces métriques, limites, comparaison des distances
• Espaces topologiques, bases, intérieur, adhérence, application continue, topologie produit, topologie induite, homéomorphismes, invariants topologiques
• Connexité, connexité par arcs, composantes connexes
• Compacité dans les espace métriques, complétude, topologie des espace vectoriels normés
  

Conditions d’examen:

Contrôle continu (sous forme écrite) et examen écritExam modalities for the first session Examen écrit
Exam modalities for the retake exam Examen écrit

Absence plan None

Task 1: Written exam, midterm and quizzes.
Assessment rules: No document or devices are allowed during exams.
Assessment criteria: Graded out of 20 for each exercise.
Weight for the final grade : Max entre le final et la moyenne pondérée du final (45%) du midterm (40%) et des 3 quizzes (15%)

• Course title: Physics didactics 1
• Number of ECTS: 3
• Course code: BA_PHYS_GEN-36
• Module(s): Module Electives 3.4 (5 ECTS required to close the module)
• Language: FR, DE, EN
• Mandatory: No

• Discover the richness of teaching physics


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


• analyse own performance to appreaciate challenges posed by teaching�� ( insight into�� 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.��

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 students��
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 ����ͷ��versity 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 edition��
Journals:�� Physik in unserer Zeit, Praxis der Naturwissenschaften, The Physics Teacher, American Journal of Physics,..��

• Course title: Theoretical physics 3 : Quantum mechanics
• Number of ECTS: 6
• Course code: BA_PHYS_GEN-18
• Module(s): Module 4.1
• Language: EN, FR
• Mandatory: Yes

The main idea of the course is to teach students on using the mathematical formalism of quantum mechanics for solving different QM problems.

During this course the students get basic knowledge on quantum mechanics (QM), which is normally assumed for Bachelor students

Review of the classical lagrangian and hamiltonian mechanics: configuration space, least action principle, Legendre transform,
Hamilton equations, Poisson Brackets, Liouville equation;
��-Double slit experiment: breakdown of classical mechanics and path-integral interpretation of interference pattern.
��-History of quantum mechanics: black body problem, UV catastrophe, Rayleigh-Jeans and Planck distributions, photoelectric effect and Einstein interpretation, concept of photon, Hydrogen spectrum and Bohr model of the hydrogen atom, Compton effects, de Broglie wave mechanics, Schrödinger wave equation, Heisenberg matrix QM, Copenhagen interpretation of QM;
��-The conceptual and mathematical structure of QM: Hilbert space of quantum states and its properties, commutator of linear operators, algebra of quantum observables (hermitian operators), and its representation in Hilbert space (algebra of Hermitian operators), change of representation, and projectors on eigenspace of hermitian operators. Expectation value of an operator on a given state. ����ͷ��tary evolution and Stone’s Theorem. Conceptual aspects of the uncertainty principle in QM for the canonical conjugate variable.
��-QM of 1D systems: stationary and time-dependent Schrödinger equation, eigenvalues and eigenstates of the Hamiltonian operator, degeneracy of eigenvalues, Node theorem, free particle on the real axis and in a finite domain with periodic boundary conditions. Plane waves, Fourier transforms, and change of representation between canonical conjugate variables.
��-1D Quantum Harmonic oscillator: eigenvalues and eigenstates of the Hamiltonian operators, Hermite polynomials, ground-state energy, and comparison with the classical harmonic oscillator. Creation/annihilation operators algebra;
��-QM of 2D and 3D systems: separation of variables in Schroedinger equation, angular momentum theory 2D and 3D rigid rotor, Hydrogen atom Hamiltonian and its eigenvalues and eigenstates (Laguerre Polynomials). Pauli Spin and exchange interactions.
��-Approximate methods: Time independent perturbation theory, Stark effect, Variational principle, Selection rules, Time-dependent perturbation theory, Fermi golden rule.
The exercise course covers many mathematical demonstrations and applications of the arguments presented in the theoretical course.

The total grade is calculated in the following way
TOT = 0.1 x AL + 0.1 x AE + 0.1 x WE + 0.2 x HW + 0.2 x IE + 0.3 x FE
AL – Attendance to Lectures, AE – Attendance to Exercises, WE – Work in Exercises, HW – Home Work, IE – Intermediate Exam, FE – Final Exam.
The attendance of students will be controlled by teachers. Any absence with an admissible excuse should be reported. Otherwise points are lost.
For each aforementioned item, the maximal possible grade is 20.�� Thus, the maximal possible total grade is also 20. To pass the course, one needs to gain at least 10 points for total grade.����
The retake exam is considered as the Final Exam, but with a possibly increased complexity of tasks.

The bibliography includes:
– Stephen Gasiorowicz, “Quantum Physics”;
– David J. Griffiths and Darrell F. Schroeter, “Introduction to Quantum Mechanics”;
– J. J. Sakurai, “Modern quantum mechanics”;��
– “Quantum mechanics and path integrals”,
R. Feynman and A. Hibbs

��

• Course title: Advanced lab course (Lab course 3+4)
• Number of ECTS: 8
• Course code: BA_PHYS_GEN-19
• Module(s): Module 4.2
• Language: EN
• Mandatory: Yes

The students get to know physical laws and relations by conducting experiments.

Consolidation of the knowledge gained in the theoretical undergraduate courses.
Getting skills in experimental physics.
Learning how to deal with experimental errors.
Learning how to write scientific reports.
Evaluate experimental data with the computer.

The students work in groups of two or maximum three persons during the experimental sessions. They conduct at least eight physical experiments from the following list:
Quantization
Heat conductivity
X-Rays
Crystallography
Magnetic properties of atoms (ESR, NMR)
Zeeman Effect
Thermal Machines
Mechanical properties
Optical tweezers
LASER
For each experiment, each group must write a report which is graded.
Twice the semester, the students must pass an oral exam.

Continuous evaluation.
The final mark consists of��
Evaluation of the conduction of the practical works during the sessions and evaluation of the reports for each experiment (33.3%)
Each of the 8 reports must be graded with at least 10/20 in order to pass the course. The students have the possibility to resubmit improved versions of reports if grading is too low.��
Evaluation of two oral exams during the semester (66.7%)
Retake exam is not possible (continuous evaluation).

Support:
Description of the experiments and additional information/literature made available on the courses’ Moodle page.
Literature:
Description of the experiments and additional information/literature made available on the courses Moodle page.

• Course title: Chemistry 2
• Number of ECTS: 2
• Course code: BA_PHYS_GEN-20
• Module(s): Module 4.3
• Language: EN, DE
• Mandatory: Yes

To make sure that the student knows all the hazards which are possible during the laboratory experiment that they are about to undertake.
To make sure that the student understands what to do, and what the experiment is about. Before actually doing the experiment, the student will be tested.
Students must make an experimental report of what they have done, observed, and understood. A full description of the objectives is given in the laboratory guide which all students are given at the beginning of the course.
(i) To learn how to carry out chemical experiments safely;
(ii) learn how to report on chemical experiments;
(iii) understand and carry out standard chemical experiments; (iv) introduce standard chemical procedures.

A student who successfully completes this course as instructed will be able to (i) research and assess the safety of the experiment that they are about to carry out (ii) write a scientific report including relevant abstract, introduction, method, results, conclusion and bibliography respecting plagiarism rules (iii) use a weighing balance, pipette, various solution manipulations, thin layer chromatography, UV-VIS and IR spectroscopy amongst others (iv) quantify the concentration of a known chemical, assess the speed of a reaction, carry out an organic work up and be able to separate two compounds dissolved in the same solution.

There are five experiments carried out over six weeks.
Before every experiment there is a preparation lecture.
The five experiments are (1) acid – base titration (2) calibration curve for unknown concentration (3) organic experiment kinetics (4) organic acidic workup (5) chromatography. After the experiment��

Class attendance is mandatory. A student may be excused, and a session rescheduled, only upon presentation of a medical certificate submitted within three days of the absence.
First session
Before each experiment, students must complete two quizzes on Moodle. Failure to do so will result in exclusion from the laboratory session and they will not be permitted to perform the experiment.
After each experiment, students must answer the written proforma questions related to the experiment just completed. They then have two weeks to submit their report.
The final grade is calculated as the weighted average of the scores obtained from the five written proforma tests, the five laboratory reports, and the final exam (approximately 30 minutes in the chemistry laboratory). The final exam assesses the student’s safety and ability in the laboratory, based on the training that they have received.
Retake exam��

Absence plan��
Only for
Continuous evaluation
Combined evaluation (final exam and continuous evaluation)
Students who fail to submit their report by the given deadline will receive a score of zero. An extension may be granted only upon presentation of a valid medical certificate, which must be submitted before the deadline.
Students who do not attend the exam will receive a score of zero. Rescheduling of the exam is possible only upon presentation of a medical certificate submitted before the deadline.

1.If justified absent during midterms, an ————————————————————-.
2.If justified absence during the written exam, an ————————————————-.

A lab guideline book will be given to each student. Own research is required

• Course title: Introduction to Biological Physics
• Number of ECTS: 4
• Course code: BA_PHYS_GEN-43
• Module(s): Module 4.3
• Language:
• Mandatory: Yes

Upon successful completion of the course, a student will have fundamental understanding of the topics covered during the course, including but not limited to:
– basic units of life and cellular complexity
– structure, dynamics and functions of units of life��
– mechanistic understanding of biological units leading to systems and processes��
– understanding biological systems using physical models (examples)
– hierarchical and emergent organization of living systems

We are living in the “Age of Biology”, where quantitative approaches from Physics are playing an increasingly crucial role in decoding the intricacies of biological systems and their diversity of structure, dynamics and functions. This course will provide an introduction to the field of Biological Physics, and equip students to study biological systems and processes using basic tools and techniques from the domain of Physics.

Continuous evaluation:

On each tutorial session, the students take a quiz based on the topics covered during the lectures preceeding the tutorials. The students will have 5 such quizes over the course of the semester. The performance in the quizes will account for 80% of the final score in the semester.

At the end of the semester, the students will do a presentation (in groups of 2-3): this will account for 20% to the final score.

No specific textbook will be followed, though for each topic relevant references will be suggested. Students are encouraged to take lecture notes; in some cases printed reading materials will be distributed during the lectures. Students can optionally follow Physical Biology of the Cell by Phillips, Kondev, Theriot and Garcia (ISBN: 0815344503).

• Course title: Didactics for Physics 2
• Number of ECTS: 3
• Course code: BA_PHYS_GEN-26
• Module(s): Module options 4.4 (10 ECTS required to close the module)
• 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 ����ͷ��versity Press, 2001, ISBN: 978-0335-197699
H.K. Mikalsis (Hrsg.) Physik Didaktik, Cornelsen Scriptor, 2006, ISBN: 378-3589221486

• Course title: Introduction to Geophysics: Learning to think like a scientist
• Number of ECTS: 2
• Course code: BA_PHYS_GEN-42
• Module(s): Module options 4.4 (10 ECTS required to close the module)
• Language:
• Mandatory: No

The module will develop your understanding of Earth. We will use MATLAB to explore the different types of geophysical data to understand the physical properties of the Earth. Students will learn how
Search the Web for different types of geophysical data
Use MATLAB for data analysis
Create 2D plots of time series data
Understand the mean, scatter, and trend of geophysical time series
Fit seasonal signals and calculate residuals
Use geophysical data to measure plate tectonic velocities and estimate natural hazards
Plot and interpret the pattern of seismicity globally in terms of plate tectonics
Determine their location on Earth using GNSS
Understand mass changes on Earth from satellite gravity observations.

Students that successfully complete this course will be able:
To understand why the Earth looks like it does
To understand why earthquakes and volcanoes occur where they do
To understand how to use GNSS to measure plate velocities
To understand how GNSS and satellite gravity can tell us about Earth

Class Outline: Questions to Explore
How do geophysicists approach problem-solving and analysis?
What is the step-by-step process of the scientific method in geophysics?
What roles and tasks are undertaken by geophysicists in their field?
In what ways can MATLAB be effectively used to enhance our understanding of Earth’s dynamics?
What fundamental principles define plate tectonics and its role in shaping the Earth’s surface?
Why do seismic activities like earthquakes and volcanic eruptions occur in specific geographical locations?
What is the significance of satellite geodesy in geophysical research, and how does it contribute to our understanding of Earth?
How does GNSS play a key role in investigating seismic hazards, and what insights can be gained from such studies?
Distinguish between absolute gravity and relative gravity, and understand their respective applications in geophysics.
Explore the methodologies involved in measuring mass changes from space and the reasons behind these measurements.
What is optical imaging, and how does it serve as a tool for comprehending Earth’s processes and features?
What key components constitute the water cycle, and how does it influence various Earth processes and ecosystems?

Task 1: Written exam during exam session (45%)
Task 2: Home-Assignment and Project (45%)
Assessment Rules: Submission of reports via Moodle within the stipulated timeframe.
Assessment Criteria: Graded out of 20 for each exercise, assessing depth of understanding, application of concepts, and overall quality of work.
Task3: Participation (10%)
Assessment Rules: Active and constructive engagement in class activities, discussions, and collaborative projects.
Assessment Criteria: Evaluation based on the frequency and quality of contributions, demonstrating a commitment to the learning process.

To be defined in the lecture as required.

• Course title: Probabilités et statistique appliquée pour ingénieurs et physiciens 2
• Number of ECTS: 3
• Course code: BA_PHYS_GEN-25
• Module(s): Module options 4.4 (10 ECTS required to close the module)
• Language: EN
• Mandatory: No

The course is meant to present some advanced topics of probability theory, such as laws of large numbers and central limit theorems, and to illustrate them through several concrete examples. The instructor will then apply these notions in order to introduce and develop some basic concepts of statistical inference.����

Le cours vise à présenter quelques sujets avancés de la théorie des probabilités, tels que les lois des grands nombres et les théorèmes centraux limites, et à les illustrer à travers plusieurs exemples concrets. On appliquera ensuite ces outils afin d’introduire et de développer quelques notions de base de l’inférence statistique.

At the end of the course, the student will (i) understand the significance and use of probabilistic limit theorems (law of large numbers, central limit theorem); (ii) be able to apply the limit theorems at Point (i) to a number of concrete examples; (iii) understand and be able to apply the basic concepts of statistics, such as parameter estimation, confidence intervals and hypotheses testing.��



A l’issue du cours, l’étudiant (i) aura compris la signification et l’utilisation des théorèmes probabilistes limites (loi des grands nombres, théorème central limite) ; (ii) sera capable d’appliquer les théorèmes limites du point (i) à un certain nombre d’exemples concrets ; (iii) comprendra et sera capable d’appliquer les concepts de base des statistiques, tels que l’estimation des paramètres, les intervalles de confiance et les tests d’hypothèses.

After having reviewed some foundational notions (discrete and continuous random variables, densities, distribution functions, moment computation) the course will introduce the student to some advanced topics in probability theory, connected, in particular, to laws of large numbers and central limit theorems. In the second part of the lectures, several fundamental notions of statistical inference will be defined and illustrated through a number of examples.

Written exam

��

Examen écrit

Lecture notes prepared by the instructor.




Notes de cours.

• Course title: Data Science and Machine Learning in Physics
• Number of ECTS: 3
• Course code: BA_PHYS_GEN-24
• Module(s): Module options 4.4 (10 ECTS required to close the module)
• Language:
• Mandatory: No

A student should be able to:
��
– Design and query a database
– Smooth and feature-extract data using filters in direct and Fourier space
– Extract features from high-dimensional data using Principal Components Analysis
– Identify structures in data by clustering
– Write down a Bayesian belief network
– Design and train a perceptron�� for a classification task
– Demonstrate an understanding of self-organisation during the training process
– Demonstrate an understanding of error propagation in a deep learning engine
– Apply kernel machines to train non-linear maps
��

��
Learning outcomes
– Understand numerical data as defining a family of structures in spaces
– Understand soft, probabilistic and bio-mimetic reasoning methods
– Understand approximation of probability distributions by nonlinear models
��

��Data science is looking for patterns in large data sets.
Machine learning is developing or fitting nonlinear models of many parameters (which may require large data sets).
��
Feature discovery:
�� �� – Fourier analysis & filters :�� �� �� �� �� �� 1 lesson
�� �� – Principal Components Analysis:�� 1 lesson
�� �� ��– Clustering algorithms:�� �� �� �� �� �� �� �� ��2 lessons
Machine Learning:
�� �� – Multilayer neural networks :�� �� �� ��2 lessons
Statistical Modelling:
�� �� – Bayes’ rule:�� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� �� ��1 lesson
�� �� – Properties of distributions:�� �� �� �� �� �� �� �� 2 lessons
�� �� – Probabilistic logic and contingency: 2 lessons
��

Continuous
Weekly tasks are given out and assessed.�� Tasks will include python programming assignments, preparation and discussion of plots and graphs, and writing of derivations.

��
https://en.wikipedia.org/wiki/Dimensionality_reduction
��
https://en.wikipedia.org/wiki/Cluster_analysis
��
https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf
��

• Course title: Analyse 4b
• Number of ECTS: 2
• Course code: BA_PHYS_GEN-22
• Module(s): Module options 4.4 (10 ECTS required to close the module)
• Language:
• Mandatory: No

Initiation to partial differential equations. Mathematical understanding of the heat and wave propagation on finite and infinite domains.

Students who successfully followed the course Analysis 4b will be capable of:
-Solving various types of partial differential equations (as first order PDEs, wave equation, and heat equation) and qualitatively interpreting the solutions.
-Modeling various physical phenomena by partial differential equations.
-Applying the knowledge to simple physical problems.

1. First order PDEs.��
Surfaces, vector fields, integrable curves. Method of characteristic curves for solving first order PDEs. Non-global solutions and shock waves.
2.�� Wave equation.
D’Alembert solution of the wave equation on the whole line. External forcing and resonance. Causality and energy.
3. Heat or diffusion equation.
Physical interpretation. General solution on the whole line. Maximum principle and stability. Distributions.
4. Boundary problems.
Wave and diffusion equation on a finite string. Separation of variables. Dirichlet, Neumann, and Robin boundary condition. Physical interpretation.
5. Fourier series.
Sine, cosine, and full Fourier series. Application to the initial-boundary value problem. Convergence and Gibbs phenomenon.

Continuous assessment and final exam.

Support / Literature

Partial Differential Equations: An Introduction, Walter A. Strauss, John Wiley Sons, 2007.
– Introduction to Partial Differential Equations, Peter J. Oliver, Springer, 2014.
-Partial Differential Equations, Lawrence C. Evans, AMS, Providence, Rhode Island, 2010.
Information after course registration

��

• Course title: Logiciels mathématiques
• Number of ECTS: 3
• Course code: BA_MATH_GEN-13
• Module(s): Module options 4.4 (10 ECTS required to close the module)
• Language: EN
• Mandatory: No

The first part of the course will cover the basics of the LaTeX markup language.
We will see how to use it to write a mathematical text, such as lecture notes or a Thesis, and to prepare slides for a presentation.
In the second part we will focus on SageMath and other mathematical software to carry out computations. We will also briefly talk about computational complexity and how to write more efficient code.

The student who completes the course will be able to use the LaTeX markup language to write documents and prepare slide-based presentations and to use SageMath and other mathematical software to carry out computations.

LaTeX [1] is a markup language to write and format documents of any type. It is particularly well-suited for scientific documents, but it can be used for any type of document, including books, CVs and even presentation slides.
It can be used together with a graphical front-end (such as TexMaker, TexStudio, Overleaf…) to immediately see the pdf output. The main advantage over a more classical word processor such as Microsoft Word, besides a much better support for writing mathematical formulas and theorems, is that in LaTeX “What you see is what you mean” [2]: by typing commands instead of visually changing the appearence of the text, the “compiler” will always try to produce an output that is faithful to what the user indicated, so the user does not have to manually adjust the result after every major modification.
SageMath [3] is a free and open-source Mathematical software system which builds on top of many existing: NumPy, SciPy, matplotlib, Sympy, Pari/GP, GAP, R and many more. Thanks to it, all the features all these languages can be accessed from a common python-based interface.
In practice, the SageMath “language” is almost identical to python, but it provides a complete set of libraries to deal with many mathematical objects and computations.

1. https://en.wikipedia.org/wiki/LaTeX
2. https://en.wikipedia.org/wiki/WYSIWYM
3. https://www.sagemath.org/

��

The evaluation of the course will be based Quiz and Final project.��

Note

https://doc.sagemath.org/html/en/tutorial/index.html

��

• Course title: Analyse numérique pour ingénieurs et physiciens
• Number of ECTS: 4
• Course code: BA_PHYS_GEN-23
• Module(s): Module options 4.4 (10 ECTS required to close the module)
• Language: FR
• Mandatory: No

Voir “learning outcomes”

Au terme du cours, l’étudiant doit être à même de:
comprendre le rôle central de l’analyse numérique dans les sciences mathématiques pures et appliquées;
maitriser les notions et les algorithmes fondamentaux de l’analyse numérique (approximation de fonctions, résolution d’équations, calcul approché d’intégrales);
acquérir un raisonnement rigoureux et systématique, indispensable à l’analyse et à l’interprétation des objets étudiés en analyse numérique;
formuler et résoudre mathématiquement certains problèmes numériques modélisables au moyen de l’analyse mathématique et de l’algèbre linéaire

Normes d’opérateurs
Approximation polynomiale
Résolution d’équations non linéaires
Résolution numérique des systèmes linéaires
Intégration numérique
Résolution numérique d’équations différentielles

examen écrit en fin de semestre

Retake: Examen écrit

Des notes de cours ou des slides sont disponibles sur la plateforme Moodle