The department of Mathematics and Computer Science, Eindhoven University of Technology is looking for a postdoctoral candidate who will work on Optimal Transport & Machine Learning Methods for Inverse Problems within the new research group on Data-Driven Scientic Computing.
The goal of the group is to develop algorithms mixing the strengths of physics-based PDE methods with the ones offered by data-driven machine learning approaches. Both strategies have classically been considered separately, despite that they often provide complementary descriptions of the same reality. The group will address the growing need to combine them in an optimal way, using strategies that will depend on the application.
The candidate’s research project will consist in developing numerical methods for inverse problems where the goal is to recover the state of a physical system based on a limited set of noisy partial observations. The study will focus on physical phenomena that are modeled with high-dimensional PDEs involving strong advection effects. Examples of such equations can be conservation laws, transport or kinetic equations, Fokker-Planck equations, or Mean Field Game Equations.
Salary
The Postdoc on Optimal Transport & Machine Learning Methods for Inverse Problems offers:
- A gross monthly salary and benefits in accordance with the Collective Labor Agreement for Dutch Universities. The gross monthly salary depends on the candidates’ experience.
- Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
- A broad package of fringe benefits (including an excellent technical infrastructure, moving expenses, and savings schemes).
- Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children’s daycare and sports facilities.
Job Requirements
The ideal candidate for Postdoc on Optimal Transport & Machine Learning Methods for Inverse Problems will have the following skills:
- PhD degree in Applied Mathematics (Numerical Analysis, Applied Analysis, Scientific Computing), Statistics, or Machine Learning.
- Interest and some knowledge in at least one of the following topics: Optimal Transport, Machine Learning, Inverse Problems, Data-Assimilation, Optimization, Numerical Schemes.
- for Conservation Laws or Mean Field Games, Model Order Reduction of parametric PDEs.
- Provable coding experience in Python, Julia, or C++.
- Interest in teaching activities.
- Strong interpersonal, organizational, and communication skills. Ability to work both independently and in a team.
- Working and teaching are in English so excellent skills in this language are required.
Application Process
The application for Postdoc on Optimal Transport & Machine Learning Methods for Inverse Problems should include a:
- Cover letter describing candidates’ motivation and qualifications for the position.
- Curriculum vitae, including a list of their publications and the contact information of three references.
- List of five self-selected ‘best publications’.
To apply for the Postdoc on Optimal Transport & Machine Learning Methods for Inverse Problems, click here.
Application Deadline: June 15, 2022.
For more information, visit the official site.
