"Scientist in Applied Mathematics, Modeling, and Optimization"

Position Summary:

The UM6P Vanguard Center & The School of Applied Sciences & Engineering at UM6P invite applications for a Scientist in Applied Mathematics, Modeling, and Optimization to contribute to the interdisciplinary Wicked Problems (WiP) framework in the undergraduate program. This role integrates mathematical modeling, numerical simulation, and decision optimization to help students understand, abstract, and solve complex real-world systems, ranging from climate, health, and manufacturing to logistics, mobility, and urban design.

Key Responsibilities:

• Mentor student teams in the mathematical modeling and optimization of dynamic, uncertain, and multiscale systems.
• Guide the design and implementation of numerical algorithms, optimization routines, and simulation-based decision tools.
• Support project-based learning with practical applications in PDEs, ODEs, discrete optimization, and control theory.
• Provide expertise in operations research methods (e.g., linear, nonlinear, integer, stochastic programming) and computational mathematics (e.g., finite elements, reduced-order models).
• Co-develop open-source tools, educational notebooks, and visual interfaces for mathematical exploration and experimentation.
• Publish research and co-author student-led publications resulting from WiP investigations.
• Contribute to applied research motivated by the WiP investigations.

Required Qualifications:

• Ph.D. in Applied Mathematics, Operations Research, Scientific Computing, Engineering, or a closely related field.
• Proven expertise in mathematical modeling and numerical optimization techniques.
• Proficiency with programming languages such as Python, MATLAB, Julia, or C++.
• Experience developing and applying algorithms for simulation, optimization, or decision analysis.
• Strong commitment to interdisciplinary education and student mentoring.

Desirable Qualifications (at least two of the following):

• Experience with PDE-constrained optimization, optimal control, or inverse problems.
• Familiarity with numerical libraries (e.g., FEniCS, Gurobi, Pyomo, deal.II, PETSc).
• Background in real-world systems such as smart manufacturing, energy networks, or biological systems.
• Interest in hybrid modeling (e.g., combining physics-based models with machine learning).
• Experience co-developing tools or interactive content for STEM education.

Cross-Domain Application Areas May Include:

• Resource allocation in energy, health, and logistics
• Simulation of physical, biological, or socio-technical systems
• Digital twin modeling and control
• Mathematical foundations of sustainability and resilience
• Intelligent decision-support systems