PhD Position F/M Bayesian optimal sensor placement using model gradients: a majorize-then-optimize strategy
Type de contrat : CDD
Niveau de diplôme exigé : Bac + 5 ou équivalent
Fonction : Doctorant
Niveau d'expérience souhaité : Jeune diplômé
A propos du centre ou de la direction fonctionnelle
The Centre Inria de l’Université de Grenoble groups together almost 600 people in 22 research teams and 8 research support departments.
Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE, …), but also with key economic players in the area.
The Centre Inria de l’Université Grenoble Alpes is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.
Contexte et atouts du poste
The PhD thesis will take place at the Université Grenoble Alpes in the Inria-AIRSEA team. This project is funded by Numpex, the French exascale supercomputing program (https://numpex.org/). Scientific collaboration with fellow academics from Numpex is anticipated.
Mission confiée
Bayesian optimal sensor placement is critical in various applications, particularly in scenarios where data acquisition is expensive (satelite observation, buoys in the ocean, underground drill etc). The primary challenge lies in determining the optimal locations where to observe the system in order to best inference a specific parameter of interest. While linear models and Gaussian priors are well-understood and relatively straightforward to handle, the problem becomes significantly more complex when dealing with models that are numerically costly to evaluate. This is especially true for large-scale, nonlinear and nonGaussian systems for which evaluating the numerical model is prohibitively expensive.
Recently, a gradient-based approach has been proposed to alleviate this computational burden. The strategy behind this approach is to minimize a bound of the so-called Expected Information Gain (EIG), which is relatively easy to work with, rather than minimizing the EIG itself. In principle, this bound serves as a surrogate for the EIG which providing a computationally favorable way to guide the sensor placement. This is because the error-bound can be evaluated and optimized much more efficiently than the actual error, which requires numerous expensive numerical simulations of the numerical model.
The objective of this project is to address various numerical aspects associated with the gradient-based solution for the Bayesian optimal sensor placement problem. The project has three main goals:
- Firstly, we seek to enhance our understanding of the majorize-then-minimize approach used in the gradient-based solution. We will achieve this by comparing the solutions obtained from the bound-based approach with those obtained from the conventional EIG-based approach. Ultimately, we hope to use the bound-based approach as a preconditioning step for the EIG-based solution to improve its accuracy.
- Secondly, we will employ randomized linear algebra methods to accelerate the computation of the bound which, in the high-dimensional setting, can still be quite expensive to compute. This will help to improve the computational efficiency of the gradient-based approach, making it more practical for large-scale systems.
- Finally, we will address the challenge of incorporating physical constraints into the sensor placement problem. Specifically, we will investigate how to take into account the constraints (physical/technical/financial) on the way the system can be observed, in order to obtain more realistic and practical sensor placement solutions.
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 (90 days / year) and flexible organization of working hours
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage
Rémunération
1st and 2nd year: 2100 euros gross salary /month
3rd year: 2190 euros gross salary / month
Informations générales
- Thème/Domaine :
Sciences de la planète, de l'environnement et de l'énergie
Calcul Scientifique (BAP E) - Ville : Montbonnot
- Centre Inria : Centre Inria de l'Université Grenoble Alpes
- Date de prise de fonction souhaitée : 2024-09-01
- Durée de contrat : 3 ans
- Date limite pour postuler : 2024-07-20
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
Applications must be submitted online via the Inria website. Processing of applications submitted via other channels is not guaranteed.
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 : AIRSEA
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Directeur de thèse :
Zahm Olivier / olivier.zahm@inria.fr
L'essentiel pour réussir
This PhD project lies at the intersection of Computer Science and Uncertainty Quantification. Candidates must possess strong knowledge in at least one of these fields and be motivated to quickly implement numerical applications. The research will involve both theoretical developments and practical coding. Candidates should demonstrate experience and skills in several of the following areas: scientific creativity, autonomy, writing abilities, oral communication skills (in English, and possibly French), and a passion for teamwork. The balance between theoretical and applied work can be adjusted according to the candidate's preferences.
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.