Activez les alertes d’offres d’emploi par e-mail !
PhD Position F/M Online Learning with Limited Resources
Inria
Nice, Montpellier
Sur place
EUR 60 000 - 80 000
Plein temps
Générez un CV personnalisé en quelques minutes
Décrochez un entretien et gagnez plus. En savoir plus
Résumé du poste
Une entreprise de recherche de premier plan recherche un candidat passionné pour un doctorat en apprentissage en ligne. Ce poste offre une occasion unique de travailler sur des algorithmes d'apprentissage optimisés pour des environnements à ressources limitées, en collaboration avec des universités et des industries de renommée mondiale. En rejoignant cette initiative, vous participerez à des projets innovants qui façonnent l'avenir de l'apprentissage automatique, tout en bénéficiant d'un environnement de travail flexible et d'un ensemble d'avantages attrayants. Si vous êtes motivé par les défis mathématiques et l'innovation, cette opportunité est faite pour vous.
Prestations
Qualifications
- Solide formation en mathématiques, en particulier en optimisation.
- Connaissances en apprentissage automatique et compétences en programmation.
Responsabilités
- Recherche sur les algorithmes d'apprentissage en ligne optimisés.
- Développement d'algorithmes robustes pour des environnements à ressources limitées.
Connaissances
Formation
Description du poste
Level of qualifications required: Graduate degree or equivalent
Function: PhD Position
The Inria center at Université Côte d'Azur includes 42 research teams and 9 support services. The center’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians, and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regional economic players.
With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.
The position is part of a new Marie Curie Training Network called FINALITY, in which Inria joins forces with top universities and industries, including IMDEA, KTH, TU Delft, the University of Avignon (Project Leader), the Cyprus Institute, Nokia, Telefonica, Ericsson, Orange, and others. The PhD students will have opportunities for internships with other academic and industry partners and will be able to participate in thematic summer schools and workshops organized by the project.
Only people who have spent less than one year in France in the last 3 years are eligible.
The candidate will receive a monthly living allowance of about €2,735, a mobility allowance of €414, and, if applicable, a family allowance of €458 (gross amounts).
This thesis focuses on advancing online learning algorithms that offer theoretical guarantees against an adversary who selects the sequence of inputs with the goal to jeopardize system performance. Such adversarially robust algorithms are particularly beneficial for scenarios characterized by highly dynamic user demands and/or rapidly evolving network conditions.
A key metric in evaluating the robustness of these algorithms is regret, which measures the largest discrepancy between the algorithm's experienced cost and that of the optimal static policy in hindsight (i.e., one that has prior knowledge of the entire input sequence). The objective is to develop algorithms with sublinear regret growth relative to input sequence length, ensuring that their per-input-average cost asymptotically approaches that of the best static policy.
Online gradient descent, follow-the-perturbed-leader or follow-the-regularized-leader exemplify algorithms that achieve sublinear regret in practical applications. However, their computational and memory requirements often exceed the capacities of edge devices and/or are incompatible with tight latency constraints, largely due to large state storage and/or projection operations over the feasible state space.
This thesis aims to design online learning algorithms optimized for reduced memory and computational overhead, making them more suitable for resource-constrained and latency-sensitive environments. Initial strategies for complexity reduction include batch processing of inputs, input sampling, and constraint relaxation. Building on these approaches, this work will explore novel methods to further streamline these algorithms while preserving robust performance.
Research
The candidate should have a solid mathematical background (in particular on optimization) and in general be keen on using mathematics to model real problems and get insights. He should also be knowledgeable on machine learning and have good programming skills. We expect the candidate to be fluent in English.
- 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
- Social, cultural and sports events and activities
- Access to vocational training
- Contribution to mutual insurance (subject to conditions)
The candidate will receive a monthly living allowance of about €2,735, a mobility allowance of €414, and, if applicable, a family allowance of €458 (gross amounts).
Applications must be submitted online on the Inria website. Collecting applications by other channels is not guaranteed.
Warning: You must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed.
Defence Security:
This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST). Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.
Recruitment Policy:
As part of its diversity policy, all Inria positions are accessible to people with disabilities.
plus rapidement
Suivez-nous
