Post-Doctoral Research Visit F/M Stochastic non-smooth optimization by first-order methods

Post-Doctoral Research Visit F/M Stochastic non-smooth optimization by first-order methods Download job offer in PDF format Contract type : Fixed-term contract Level of qualifications required : PhD or equivalent Fonction : Post-Doctoral Research Visit Level of experience : Recently graduated About the research centre or Inria department The Centre Inria de l’Université de Grenoble groups together almost 450 people in 26 research teams and 9 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. Context The postdoc will be conducted between the Inria GHOST team and the DAO team of the LJK lab. It will be funded by the Structured Optimization and Learning chair of the MIAI cluster. The main objective is to analyze and deepen our understanding of adaptive optimization algorithms which adapt their hyperparameter (e.g., the step size) based on the data accumulated during the optimization process (e.g., during training in a learning context). This family includes central algorithms such as AdaGrad, RMSProp, Adam, and related variants, which have a key role in the recent development of numerical optimization. The majority of existing work considers these algorithms applied on smooth functions, and we want to extend this understanding to stochastic, non-convex, and non-smooth optimization. Those results will contribute to narrow the gap between current theory and the practical applications in which these algorithms are already used, which are non-convex, non-smooth. Assignment The recruited candidate will work by Mathieu Besançon (Inria, LIG), Quoc-Tung Le (UGA, LJK) and Jérôme Malick (CNRS, LJK). To understand the topic: Some important algorithms studied in the smooth case are currently ill-understood when applied to nonsmooth instances. One of the research questions will focus on the definition of a problem class close to learning models and on which nonsmooth algorithms can be analyzed. The postdoctoral researcher is expected to conduct research on AdaGrad algorithms in the nonsmooth setting. Collaboration: Some collaborations with the Zuse Institute Berlin and the Institut Mathématique de Toulouse will be possible during the working period. Main activities Principal activities: - Identify necessary/sufficient conditions so that adaptive algorithms can converge. - Investigate the robustness of the optimization methods, i.e., study qualitative and quantitative properties under the effects of noises, initialization, initial step-size. - Comparing these properties of adaptive algorithms with those of classical ones, such as (stochastic) gradient descent. - Identify a zoology of structured optimization problems, where adaptive algorithms can significantly outperform other families of methods. Complementary activities: - Disseminating findings via articles, seminars and conferences. Skills Foundation knowledge in numerical optimization, in particular on first-order methods. Scientific programming skills (Julia, Python) for empirical validations of algorithms. Benefits package • 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 under conditions Remuneration 2927 € gross salary / month Apply for this position Share • Facebook • Linkedin • Twitter • Email General Information • Theme/Domain : Optimization, machine learning and statistical methods Scientific computing (BAP E) • Town/city : Saint Martin d'Herès • Inria Center : Centre Inria de l'Université Grenoble Alpes • Starting date : 2027-01-01 • Duration of contract : 2 years • Deadline to apply : 2026-11-14 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. Instruction to apply Applications must be submitted online via the Inria website. Processing of applications submitted via 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. Contacts • Inria Team : GHOST • Recruiter : Besancon Mathieu / mathieu.besancon@inria.fr The keys to success Scientific curiosity, analytical skills to understand convergence analysis and lower bounds for numerical optimization algorithms. About Inria Inria, the French national institute for research in digital science and technology, supports the French government in national research and innovation strategies in the digital field, acting as Digital Programs Agency. Inria leads over 300 research and innovation projects with its 3,500 scientists, engineers, and support staff, in partnership with universities and the digital ecosystem (businesses, entrepreneurs, and public stakeholders). Together, we explore strategic fields such as artificial intelligence, cybersecurity, quantum computing, cloud technologies, digital transformation in healthcare, digital twins, and digital technologies for defence. We develop practical solutions such as software, tech startups, partnerships with national companies, and cutting-edge training programmes. Our goal is to drive scientific, technological, and industrial excellence to ensure France’s digital sovereignty.