Job description
NETWORKS-COFUND Postdoctoral (NETCO-PD) Position in approximate and exact operations research methods for stochastic networks. The (Data-Driven) Stochastic Operations Research groups of the department of Mathematics and Computer Science at TU/e invite applicants for a postdoctoral position in approximate and exact operations research methods for stochastic networks, e.g., structural & convergence properties of reinforcement learning algorithms, clustering in block Markov chains, performance & reliability analysis of dynamic flow networks. The position begins on October 1, 2024. The appointment will be for a term of 18-24 months. Applications will be considered (and decisions made) on a rolling basis.
This position receives funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement Grant Agreement No 101034253.
In order to be eligible, the candidate should be in possession of a PhD degree or obtain one within three months at the starting date of the appointment. The programme focuses on training researchers in the early stages after their PhD, so the candidate should have at most three years full-time post-PhD-degree research experience at the date of recruitment.
Moreover, the candidate should meet the mobility requirement of the MSCA, which encourages transnational, intersectoral and interdisciplinary mobility. The mobility requirement is: The candidate may not have resided or carried out their main activity (work, studies, etc.) in the Netherlands for more than twelve months in the three years immediately before the call deadline.
Job Description
We are looking for highly motivated and independent researchers in Applied Probability and Stochastic Operations Research. We in particular seek talented researchers with expertise in approximate and exact operations research methods for stochastic networks.
We encourage candidates who not only fortify and complement the research capabilities of the (Data-Driven) Stochastic Operations Research groups at Eindhoven University of Technology, but also integrate naturally within the NETWORKS project. As such, talented researchers with (additional) expertise in algorithmics for Stochastic Operations Research will also be strongly considered.
The NETCO-PD programme has been active in efforts towards improving openness and inclusion in the (mathematical) sciences. We encourage applications from all qualified, interested applicants, and we aspire to broaden the perspectives of the group and the NETWORKS team.
The postdoctoral fellow is expected to perform the following tasks: (Task 1) contribute to the networks research themes and take a leadership role in conducting research. The research lies in the core of the appointment; (Task 2) contribute to the teaching and supervision of students in the group.
Furthermore, NETWORKS offers an extensive and inspiring training program (training week, industrial internship, professional skills courses). The research and training program of NETWORKS offers excellent opportunities for a future career, be it in academia, government, or industry.
The project will be carried out at the Mathematics and Computer Science department of the Eindhoven University of Technology. The department has a vibrant international environment, with 46% of the scientific staff being non-Dutch nationals and more than 100 PhD candidates. It has extensive experience in helping new (foreign) employees settle in.
Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.
The university is highly committed to fostering a diverse and inclusive population of students, faculty, and staff. We are especially interested in applicants who are able to work effectively with students, faculty, and staff from all backgrounds, including but not limited to: racial and ethnic minorities, women, individuals who identify with LGBTQ communities, individuals with disabilities, individuals from lower income backgrounds, and/or first-generation college graduates.