We have an opening for a PhD position. The research target is to develop machine learning methods to solve inverse problems accounting for the inherent uncertainty of the inverse problem. We will formulate these problems as inference problems with prior scientific knowledge available in the form of simulators. We will follow the simulation-based inference paradighm to model the posterior distribution of the simulators parameters that could have generated the observed data. Of partricular interest are the settings in which the domain knowledge is incomplete and or imperfect Classical approaches to simulation-based inference, e.g. Approximate Bayesian Computation, suffer from high complexity costs in particular in high dimensional spaces and machine learning has great potential to address such problems . We will build upon our recent work on generative learning approaches to solve inverse problems using forward models and simulation [2,3]. We will 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.
The successful candidate will enroll as a PhD student in the Computer Science department of the University of Geneva (under the co-direction of myself and Prof. Stephane Marchand-Maillet) and, at the same time, will become a member of the Data Mining and Machine Learning group (http://dmml.ch) at the University of applied sciences, Geneva. Starting date: autumn 2022.
The position is funded by a Swiss National Science Foundation interedisciplinaty project, with funding secured for four 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 prepared to dedicate to high quality research. The candidate should have (or be close to obtaining) a Master's degree or equivalent in computer science, statistics, applied mathematics, electrical engineering or other related field with very good background in machine learning machine learning programming environments (Pytorch, Tensorflow, JAX).
If interested, please send the following to email@example.com
- academic CV (max 2 pages)
- academic transcripts of BSc and MSc
- one page motivation letter explaining why the candidate is suitable for the position
- contact details of three referees (do not send reference letters)
Deadline for applications: 30/07/2022.
In case of any further questions, please contact firstname.lastname@example.org. K. Cranmer, J. Brehmer, and G. Louppe. The frontier of simulation-based inference. Proceedings of the National Academy of Sciences, page 201912789, 2020.
 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.
 N. Takeishi and A. Kalousis. Physics-integrated variational autoencoders for robust and interpretable generative modeling. In Advances in Neural Information Processing Systems 34, 2021b.