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  3. PhD position on Bayesian computation for non-linear inverse problems

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PhD position on Bayesian computation for non-linear inverse problems

Challenge: Enabling fast Bayesian statistics for non-linear models.
Change: Efficient sampling methods for complex physical models, such as state space models.
Impact: New state of the art computational methods for models in materials science.

3 maanden geleden

Arbeidsvoorwaarden

Standplaats:
Mekelweg, Delft, Zuid-Holland
Dienstverband:
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
32 - 38 uur
Salarisindicatie:
€ 2395 - € 3061 per maand
Opleidingsniveau:
WO

Functieomschrijving

The goal of this project is to develop new sampling methods that enable fast computation for challenging models from a Bayesian perspective with a focus on modern Markov Chain Monte Carlo (MCMC) techniques. In particular the attention will be towards highly non-linear inverse problems and state space models. In such models we may expect a strongly multi-modal posterior distribution posing particular challenges to the simulation methods. In addition, the forward problem is extremely expensive to compute and a significant part of our approach will be the use of surrogate forward models such as neural networks or Gaussian processes.

This PhD project is part of the SLIMM Lab (Statistical Learning for Intelligent Material Modeling), which studies the use of statistical learning techniques for materials science. Current state-of-the-art techniques in the modelling of materials are computationally too expensive to be used in real design situations, with relatively simple simulations easily taking months to run. Accelerating these simulations is the core goal of the SLIMM Lab. With a range of topics across four PhD projects, SLIMM will explore promising research directions on Bayesian inverse modeling and sampling (MCMC) techniques that will lead to a new generation of intelligent material models and fast multiscale simulation frameworks. For this position no prior knowledge in materials science is required.

Functie-eisen

  1. MSc degree in mathematics with a specialization or strong affinity with one of the following: inverse problems, statistics, probability, machine learning, analysis or numerical analysis.
  2. Affinity with coding numerical methods, e.g. in Python, C++, or Julia.
  3. Interest or experience in teaching and guiding students combined with a strong scientific drive.
  4. Ability to work in a multidisciplinary and diverse team.
  5. English proficiency, both verbally and in writing.

Conditions

TU Delft offers DAI-Lab PhD-candidates a 5-year contract (as opposed to the normal 4-years), with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2395 per month in the first year to € 3217 in the fifth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health

insurance and sport memberships, and a monthly work costs contribution. Flexible

work schedules can be arranged. For international applicants we offer the Coming to

Delft Service and Partner Career Advice to assist you with your relocation.

Additional information

For more information about this vacancy, please contact Hanne Kekkonen, Assistant Professor, email: h.n.kekkonen@tudelft.nl.