Clémentine Courtès

University of Strasbourg (6 months of teaching leave)

• Scientific Computing: lecture (37h), practical sessions with Python (28h) and projects
  • level: Third year, bachelor of Mathematics (selective sector: Magistère)
• Nonlinear Optimization: lecture (39h)
  • level : Third year, Mathematics and Economics dual bachelor / Actuarial Sciences

University of Strasbourg

• Nonlinear evolutionary PDEs : lecture (21h), exercices sessions (9h)
  • level : Second year of Master: PDEs and learning
• Optimization : Practical sessions with Python (6h)
  • level : First year, Master of Scientific Computing and Pure Mathematics
• Scientific Computing: lecture (37h), practical sessions with Python (28h) and projects
  • level: Third year, bachelor of Mathematics (selective sector: Magistère)
• Numerical analysis: lecture (15h), exercises sessions and practical sessions with Python (17h)
  • level: Third year, bachelor of Applied Mathematics
• Nonlinear Optimization: lecture (39h), exercices sessions (22h)
  • level : Third year, Mathematics and Economics dual bachelor / Actuarial Sciences
• Numerical analysis: lecture with Scilab (18h)
  • level: Second year, bachelor of Computer Science

University of Strasbourg

• Scientific Computing: Oral sessions (4h)
  • level: Second year of Master to prepare the competitive exam : agrégation externe
• Optimization: Practical sessions with Python (6h)
  • level: First year, Master of Scientific Computing and Pure Mathematics
• Scientific Computing: lecture (37h), practical sessions with Python (28h) and projects
  • level: Third year, bachelor of Mathematics (selective sector: Magistère)
• Numerical analysis: lecture (15h), exercises sessions and practical sessions with Python (17h)
  • level: Third year, bachelor of Applied Mathematics
• Nonlinear optimization: lecture (36h) and exercises sessions (18h)
  • level: Third year, Mathematics and Economics dual bachelor / Actuarial Sciences
• Numerical analysis: lecture with Scilab (18h)
  • level: Second year, bachelor of Computer Science
• Functions of several real variables: exercises sessions (28h)
  • level: Second year, bachelor of Physics

University of Strasbourg

• Scientific Computing: lecture (37h), practical sessions with Python (28h) and projects
  • level: Third year, bachelor of Mathematics (selective sector: Magistère)
• Numerical analysis: lecture (15h), exercises sessions and practical sessions with Python (17h)
  • level: Third year, bachelor of Applied Mathematics
• Numerical analysis: lecture with Scilab (18h)
  • level: Second year, bachelor of Computer Science
• Functions of several real variables: exercises sessions (12h)
  • level: Second year, bachelor of Physics
• Khôlles in Analysis: oral sessions (2h)
  • level: First year, bachelor (selective sector: Mathematics and Physics)

University of Strasbourg

• Scientific Computing: lecture (37h), practical sessions with Python (28h) and projects
  • level: Third year, bachelor of Mathematics (selective sector: Magistère)
• Numerical analysis: exercises sessions and practical sessions with Python (34h)
  • level: Third year, bachelor of Applied Mathematics and CAPES
• Numerical analysis: lecture with Scilab (18h)
  • level: Second year, bachelor of Computer Science
• Functions of several real variables: exercises sessions (10h)
  • level: Second year, bachelor of Physics

INSA of Toulouse

• Introduction to PDE and to Fourier theory: exercises sessions(13 h)
  • level: third year of "Construction Engineering" specialization (equiv. third year of bachelor)
• Numerical analysis for ODE: practical sessions with Python (15 h)
  • level: second year of "Construction Engineering" specialization (equiv. second year of bachelor)

University of Strasbourg

• Mathematical modelling: lecture/exercises sessions (20 h)
  • level : First year, bachelor of Mathematics and Computer science

University Paris Sud

• Scientific Computing: lecture(4 h) and oral session (6 h)
  • level: Second year of Master to prepare the competitive exam "agrégation externe"
• Analysis and numerical resolution of ODEs: lecture (13 h) and practical sessions with Python (13 h)
  • level: Third year, HEC and Mathematics dual bachelor

Polytech Paris Sud

• Numerical linear algebra, ODEs, PDEs and Scientific Computing: practical sessions with Python (9 h)
  • level: First year, engineering training at Polytech (equiv. third year of bachelor)

University Paris Sud

• Scientific Computing: practical sessions with Python (18 h)
  • level: Third year, HEC and Mathematics dual bachelor
• Introduction to Scientific Computing: practical sessions with Python (10 h)
  • level: Second year, Mathematics and Economics dual bachelor
• Analysis: exercises sessions (8 h)
  • level: First year, bachelor of MPI (Mathematics, Physics, Computer science)

Polytech Paris Sud

• Numerical linear algebra, ODEs, PDEs and Scientific Computing: exercises sessions (12 h) and practical sessions with Python (15 h)
  • level: First year, engineering training at Polytech (equiv. third year of bachelor)

University Paris Sud

• Scientific Computing: practical sessions with Python (18 h)
  • level: Third year, bachelor of MINT(Mathematics and INTeraction)
• Introduction to Scientific Computing: practical sessions with Python (10 h)
  • level: Second year, bachelor of MPI (Mathematics, Physics, Computer science)
• Analysis: exercises sessions (17 h)
  • level: First year, bachelor of MPI (Mathematics, Physics, Computer science)

Polytech Paris Sud

• Numerical linear algebra, ODEs, PDEs Scientific Computing: exercises sessions (10 h) and practical sessions with Python (11 h)
  • level: First year, engineering training at Polytech (equiv. third year of bachelor)

University Paris Sud

• Analysis and Probability: exercices sessions (32 h)
  • level: First year, bachelor of BCST (Biology, Chemistry, Earth and Life Science)
• Scientific Computing: practical sessions with C (28 h)
  • level: Third year, bachelor of MINT(Mathematics and INTeraction)