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PhD Student Position in Optimal Transport and Machine Learning

vor 2 Monaten


Berlin, Berlin, Deutschland Weierstraß-Institut für Angewandte Analysis und Stochastik Vollzeit
PhD Student Position in Optimal Transport and Machine Learning

We are seeking a highly motivated PhD student to join our research team at the Weierstraß-Institut für Angewandte Analysis und Stochastik. The successful candidate will work on a project focused on optimal transport and machine learning, with a strong emphasis on rigorous training in machine learning and optimization.

The PhD student will be part of our Data-Driven Optimization and Control group, led by Dr. Jia-Jie Zhu. The group's research focuses on developing new methods for data-driven optimization and control, with applications in machine learning, optimal transport, and other fields.

The PhD student will have the opportunity to work on a variety of topics, including:

  • Optimal transport and gradient flows for machine learning and optimization
  • Probabilistic machine learning, Bayesian inference, and sampling
  • Robustness, causal inference, and robust learning
  • Continuous optimization for machine learning, mirror descent algorithms

We are looking for candidates with a strong background in computer science, statistics, or applied mathematics, and a special focus on rigorous training in machine learning or optimization. Experience with optimal transport for machine learning applications is highly desirable.

The PhD student will receive close mentorship and support from our team, and will have the opportunity to collaborate with other researchers in the field. We offer a competitive salary and benefits package, as well as a dynamic and inclusive work environment.

**What We Offer:**

  • Close mentorship and support from our team
  • Opportunities for collaboration with other researchers in the field
  • Competitive salary and benefits package
  • Dynamic and inclusive work environment

**How to Apply:**

Please submit your application, including your CV, cover letter, and any relevant research experience or publications, via our applicant portal. We look forward to receiving your application