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Post-Doctoral Research Visit F/M Structured matrices for geometric computations

Inria -
Paris (75)

Soumettre votre candidature

Détails de l'emploi

CDD
Il y a 3 jours

Avantages

Qualifications

Niveau Doctorat
PyTorch
NumPy
Anglais
C++
Python

Description complète du poste

Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

Contexte et atouts du poste

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
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.

Mission confiée

The current implementation of KeOps provides efficient acceleration for dense, tensor-like operators. However, recent advances in biological and medical imaging, such as spatial omics, are producing a new class of high-dimensional datasets with strong underlying structures, including sparsity patterns. The goal of this postdoctoral project is therefore to move beyond bruteforce dense computations and leverage these structures to enable efficient processing of high-dimensional data.

Several research directions are possible, each tied to a specific application context. Depending on your scientific profile and interests, we will be able to focus on one or more of the following:

Strategy 1: sparse neighborhood and spatial structures: generalize block-wise reduction schemes to handle sparse matrices which have few non-zero coefficients per row. Combined with the symbolic engine of the KeOps library, this could lead to very efficient routines for geometry processing.

Strategy 2: sparse, high-dimensional features: Address the computational limitations associated to high intrinsic dimensionality by developing sparse variants of the core operations used to process such datasets. A particular emphasis will be placed on efficient GPU implementations within the KeOps framework. Applications to spatial omics data can be found in [1].

Strategy 3: low rank kernel approximation: Investigate scalable approaches to kernel matrix-vector multiplication that rely on low-rank approximations, such as the Fast and Free Memory method explored in [2].

[1] xIV-LDDMM Toolkit: A Suite of Image-Varifold Based Technologies for Representing and Mapping 3D Imaging and Spatial-omics Data Simultaneously Across Scales. K. M. Stouffer et al. Nature Commun Biol

[2] Giga-scale Kernel Matrix-Vector Multiplication on GPU. R. Hu et al. NeurIps 2022

Principales activités

Main activities :

Research work
Software development
Software documentation

Compétences

Thoroughness, with attention paid to details.
Willingness to engage in cross-disciplinary research.

Avantages

Subsidized meals
Partial reimbursement of public transport costs
Leave: 7 weeks of annual leave + 10 extra days off due 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
Social security coverage

Informations générales

Thème/Domaine : Schémas et simulations numériques
Calcul Scientifique (BAP E)
Ville : Paris
Centre Inria : Centre Inria de Paris
Date de prise de fonction souhaitée : 2026-09-01
Durée de contrat : 2 ans
Date limite pour postuler : 2026-07-01

Attention: Les candidatures doivent être déposées en ligne sur le site Inria. Le traitement des candidatures adressées par d'autres canaux n'est pas garanti.

Consignes pour postuler

Sécurité défense :
Ce poste est susceptible d’être affecté dans une zone à régime restrictif (ZRR), telle que définie dans le décret n°2011-1425 relatif à la protection du potentiel scientifique et technique de la nation (PPST). L’autorisation d’accès à une zone est délivrée par le chef d’établissement, après avis ministériel favorable, tel que défini dans l’arrêté du 03 juillet 2012, relatif à la PPST. Un avis ministériel défavorable pour un poste affecté dans une ZRR aurait pour conséquence l’annulation du recrutement.

Politique de recrutement :
Dans le cadre de sa politique diversité, tous les postes Inria sont accessibles aux personnes en situation de handicap.

Contacts

Équipe Inria : HEKA
Recruteur :
Feydy Jean / [email protected]

L'essentiel pour réussir

PhD in applied mathematics, computer science, physics, or a related field, with a strong interest in computational methods and real-world applications.
Proficiency in Python and/or a compiled language (e.g., C++, CUDA).
Strong background in at least one of the following research areas:
- Computer graphics
- Computational geometry or geometric data processing
- Scientific computing and numerical methods
- High-performance computing (HPC), GPU programming, or parallel computing
- Machine learning and kernel methods
- Optimization and large-scale linear algebra
- Sparse and/or low-rank methods for large-scale data
- Computational imaging or shape analysis
Some experience with the open-source development ecosystem (version control, collaborative workflows, software design) would be a plus.

A propos d'Inria

Inria est l’institut national de recherche dédié aux sciences et technologies du numérique. Il emploie 2600 personnes. Ses 215 équipes-projets agiles, en général communes avec des partenaires académiques, impliquent plus de 3900 scientifiques pour relever les défis du numérique, souvent à l’interface d’autres disciplines. L’institut fait appel à de nombreux talents dans plus d’une quarantaine de métiers différents. 900 personnels d’appui à la recherche et à l’innovation contribuent à faire émerger et grandir des projets scientifiques ou entrepreneuriaux qui impactent le monde. Inria travaille avec de nombreuses entreprises et a accompagné la création de plus de 200 start-up. L'institut s'eﬀorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie.

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