Post-doc position on generative models for inverse problems and simulation-based inference, Migrate project (currently open).

The Data Mining and Machine Learning group,, at the University of Applied Sciences in Geneva has an opening for a full-time post-doc position funded by a Swiss National Science Foundation interdisciplinary project. Within the project we develop models, namely generative ones, for inverse problems following simulation-based inference approaches [1]. Of particular interest are approaches in which the domain knowledge (simulator) is incomplete or imperfect. We build upon our recent work on generative learning approaches for inverse problems [2,3,4]. We explore ways to explicit encode, and complete when necessary, available domain knowledge, resulting to what is known as grey-box models. Such models respect by construction the domain knowledge, will not produce implausible outputs, and have strong extrapolation performance.

Funding is secured for up to three years. The project brings together teams from geology, seismology and machine learning with the overall objective to streamline the passive seismic exploration by developing new, machine-learning based, analysis tools in order to identify potential targets for geothermal exploitation. Within this context the DMML team will develop generative models to invert the sheer wave velocities measured in earth's surface in order to recover velocity models of the subsurface.

We seek strongly motivated candidates dedicated to high quality research. Candidates should have (or be close to obtaining) a Phd in machine learning, ideally in the area of generative modelling, and a strong research track-record attested by high quality publications in relevant machine learning venues such as ICML, NeurIPS, ICLR, AI-STATS, UAI, etc. The selected candidate is expected to demonstrate a high degree of independence and autonomy, drive their own research and actively contribute to the scientific development of the group through their knowledge and expertise as well as by proposing and contributing to group activities such as readings, schools, workshops etc. They are expected to participate in the supervision of PhD students.

The DMML group consists of roughly a dozen of researchers at the PhD and Post-doc level, working in different areas of machine learning, such as generative models, imitation learning. The team collaborates closely with the VIPER group,, from the computer science department of the University of Geneva headed by Prof. Stephane Marchand-Maillet. We offer ample opportunities and support for scientific development, e.g. providing funding for conferences, schools, research visits and exchanges etc. We strive to provide a research environment in which researchers can focus on their research and allow for space and time to develop solid ideas. The group regularly publishes to some of the best conferences in ML/AI and the alumni of the group hold research positions in academia and in GAFA companies.

If interested, please send the following to (subject: Post-Doc application, Migrate Project)
- academic CV (max 2 pages)
- pointers to their two most important publications
- one page motivation letter explaining why the candidate is suitable for the position and what they can bring to the DMML group
- 1000 word research proposal on the research topics described above
- contact details of three referees (do not send reference letters)
- copies of diplomas (PhD, MSc, BSc) and academic transcripts

Deadline for applications: 15/02/2024, starting date May 2024, negotiable.

In case of any further questions, please contact

[1] K. Cranmer, J. Brehmer, and G. Louppe. The frontier of simulation-based inference. Proceedings of the National Academy of Sciences, page 201912789, 2020.
[2] N. Takeishi and A. Kalousis. Variational autoencoder with differentiable physics engine for human gait analysis and synthesis. In NeurIPS workshop on Deep Generative Models applications, 2021a.
[3] N. Takeishi and A. Kalousis. Physics-integrated variational autoencoders for robust and interpretable generative modeling. In Advances in Neural Information Processing Systems 34, 2021b.
[4] M. Falkiewicz et al. Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability, In Advances in Neural Information Processing Systems, 36, 2023.