PhD – Explainable Causal Inference of Bayesian Dynamical Models

  • Robert-Bosch-Campus 1, 71272 Renningen, Germany
  • Full-time
  • Legal Entity: Robert Bosch GmbH

Company Description

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Job Description

Bayesian inference for dynamical systems typically rely on an (assumed) causal structure that explain how the system evolves probabilistically over time. This structure is in turn used for powerful inference. On the other hand, causal inference methods try to identify such causal relationships purely from observations and potential interventions. Additionally, prior knowledge about the underlying structure might be available, especially in dynamical systems, where such knowledge might be available in terms of (black-box) simulators.

This PhD position aims to develop novel hybrid Bayesian-causal inference methods that incorporate prior (or learned) knowledge about the causal structure and thereby improves predictions in terms of accuracy, while rendering them explainable. During your PhD, you will be part of the active research team at the Bosch Center for Artificial Intelligence (BCAI).

  • Right from day one you invent novel ways to integrate Bayesian neural networks into the causal inference setup.
  • You conduct prototypical algorithm implementations and benchmarking on real-world data sets.
  • Moreover, you publish in top-tier conferences (ICML, NIPS, ICLR, AISTATS, etc.) and journals (JMLR, PAMI, etc.).
  • You screen literature and build up close contact with the academic community.
  • Be part of the team and participate in academic interactions within the BCAI research team.        


  • Education: Excellent master degree in machine learning, mathematics, statistics, physics, computer science, or related fields
  • Personality: Enthusiastic for science, excited to tackle theoretical challenges, team player
  • Working Practice: Perform exclusively academic research with excellence (i.e. no industry project duties), gradually develop scientific independence until graduation, make significant contributions to science
  • Experience and Knowledge: General knowledge of machine learning, excellent theoretical skills proven by top course grades, prior experience on scientific writing is a plus, a background on Gaussian processes or Bayesian inference is preferable
  • Enthusiasm: Intrinsic motivation for top-level research and scientific independence
  • Languages: Fluent in English (written and spoken)

Additional Information

The final PhD topic is subject to your university. Duration: 3 years

Please submit your fully detailed resume, BSc and MSc transcripts and optionally your MSc thesis draft if it is close to final and sharable.

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Are you interested in working from home, or part-time? Please don't hesitate to ask us.
Need support during your application?
Kevin Heiner (Human Resources)
+49 711 811 12223  

Need further information about the job?
Melih Kandemir (Functional Department)
+49 711 811 52789

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