About the HeKa team at PariSanté Campus
This postdoctoral position will be hosted within the HeKA team at PariSanté Campus and supervised by the KeOps development team: Jean Feydy (Inria, HeKA), Joan Glaunès (Université Paris Cité, MAP5) and Benjamin Charlier (INRAE, MIAT).
Based at PariSanté Campus, the HeKA team is a multidisciplinary group specializing in biomedical informatics, biostatistics, and applied mathematics for clinical decision support. The team brings together researchers, clinician-scientists, and faculty members from Inria, Inserm, Université Paris Cité, and AP-HP. It also collaborates closely with several departments of the European Hospital Georges Pompidou, Necker Hospital, and the Imagine Institute.
Benefits package
Subsidized meals
- Comfortable budget for travel costs
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Partial reimbursement of public transport costs
Approximately 9 weeks of paid time off per year: 7 weeks of annual leave + 10 extra days off thanks to to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
Possibility of teleworking and flexible organization of working hours
Professional equipment available (videoconferencing, loan of computer equipment, etc.)
Social, cultural and sports events and activities
Access to vocational training
Contribution to mutual insurance (subject to conditions)
Gross Salary : 3,362 € per month
Scientific context
Massively parallel accelerators such as Graphics Processing Units (GPUs) now provide significant computational power at a fraction of the cost of a high-performance cluster. Providing user-friendly libraries that leverage these capabilities while remaining compatible with high-level development environments is essential for developping new methodological approach to analyze real-world datasets.
The KeOps library (https://kernel-operations.io/) (1M+ downloads) follows this approach and focuses on geometric computations based on the manipulation of distance and kernel matrices. These are widely used to compute interactions between large collections of samples, with applications that range from 3D shape processing to machine learning and computational physics.
KeOps introduces a high-level abstraction based on symbolic matrices (LazyTensors), offering a memory- and compute-efficient, transparent framework that is fully compatible with Python (NumPy, PyTorch) and R. We refer to this discussion (https://www.kernel-operations.io/keops/introduction/why_using_keops.html) for more details.
