The Weierstrass Institute (WIAS) in Berlin, Germany, is looking for a postdoc candidate passionate about conducting research in the intersection of mathematical optimization, machine learning, optimal control with a focus on robustness under distribution shift. The position is associated with a third-party-funded research project led by Dr. Jia-Jie Zhu (WIAS Berlin) and Dr. Michael Hintermüller (WIAS/ Humboldt-Universität zu Berlin). The initial funding period will run for two years, starting January 1st, 2022. The applicants should have completed their Ph.D. degrees by the starting date of the project.
Subject and candidate qualification
Motivated by numerous partial differential equation (PDE) related practical applications, a pressing challenge for data-driven optimization and control systems is the ubiquitous distribution shift, which implies higher demand for the robustness of the learning-for-control design. The project, funded by the Excellence Cluster MATH+: The Berlin Mathematics Research Center, aims to address data-driven robust control and optimization of dynamical systems under data distribution shifts, **using principled tools from applied mathematics and statistical machine learning as well as reinforcement learning. We invite candidates whose scholar **profiles are mainly theoretical and exhibit proven excellence in research. We specifically prefer two types of mathematical research experiences:
(1) either in principled statistical machine learning/reinforcement learning theory (related to dynamical systems, time series, Markov decision process (MDP), principled control theory). Those qualifications are demonstrated, among others, by high-quality publications in credible venues such as NeurIPS/ICLR/AISTATS/ICML/CoLT/JMLR/L4DC.
(2) and/or in applied mathematics, in optimization, numerical analysis, optimal control, dynamical systems (PDEs and S(P)DEs), data-driven modeling of dynamics, theory of model predictive control (MPC). Those qualifications are demonstrated, among others by relevant publications in credible venues such as SIAM OPT/CON/COAP/COCV/MathProg.
What we offer
- The Weierstrass Institute for Applied Analysis and Stochastics (WIAS), Berlin is a research institution known for its strength in mathematical optimization, optimal control, dynamical systems, and applied mathematics in general. It has hosted flagship conferences in mathematical optimization, such as ICCOPT 2019. We also envision collaborations with our partners at top institutions such as the Max Planck Institute for Intelligent Systems, Tübingen.
- We are located in downtown Berlin. Berlin is one of the most culture-rich and diverse international cities in the world. It offers endless opportunities to enjoy life outside work, while being very affordable compare to other major cities. The working language is English (although we offer free German courses). We highly welcome applications from qualified international, female, and minority candidates. Scientifically, Berlin offers a rich landscape with numerous opportunities for research, as well as job prospects in academia and industry.
Application and more details
To inquire about the position (highly recommended), please send the following:
- 1-2 page cover letter (including a very brief research statement)
- CV with links to your existing works
- Ph.D. thesis (or draft if not yet completed)
- Transcripts for bachelor’s, master’s, and Ph.D.
to the email address firstname.lastname@example.org, with subject containing [apply-postdoc-mathplus]. The position will be open until filled. However, for better full consideration, please submit an application as soon as possible.
To apply, candidates will
- provide information of 2-3 references who have agreed to be contacted
- submit to an official platform the above documents and official certificates, transcripts, and other awards
Please direct all inquiries to Dr. Jia-Jie Zhu using the email address email@example.com, with the subject containing [inquiry-postdoc-mathplus].
For more information about WIAS Berlin, see https://www.wias-berlin.de/.