PhD Position F/M Ph.D. student: Nonsmooth optimization for machine learning
Contract type : Fixed-term contract
Level of qualifications required : Graduate degree or equivalent
Fonction : PhD Position
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 Ph.D. research will be carried out between the GHOST team at Inria and the DAO team of the LJK laboratory.
The candidate will be funded by the research chair on Structured Optimization and Learning of the MIAI cluster.
The goal will be to develop and analyze new optimization algorithms that are well-suited to the nonsmooth nonconvex problemsthat are common in machine learning. While modern deep architectures often include nonsmooth layers, many currently popular methods are mostly studied and understood through the lens of smooth optimization.
We will therefore strive to establish theoretical guarantees on algorithms close to those used by machine learning practitioners in order to close this theory-practice gap.
Assignment
The recruited candidate will be co-supervised by Mathieu Besançon (Inria, LIG), Quoc-Tung Le (UGA, LJK) and Jérôme Malick (CNRS, LJK).
To understand the topic:
Some 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 candidate can read the recent literature on adaptive algorithms such as AdaGrad for relevant families of nonsmooth methods that will be considered.
Collaboration: Some collaborations with the Zuse Institute Berlin and the Institut Mathématique de Toulouse will be possible during the Ph.D.
Main activities
- Formalizing necessary properties for the convergence of optimization algorithms on nonsmooth functions.
- Developing new algorithms that are well-suited to problem classes close to modern learning architectures.
- Implementing and evaluating the computational performance of the proposed methods on reference benchmark problems.
Complementary activities:
- Developing open-source software implementations of optimization methods
- Writing reports and research articles
Skills
Mathematical methods for optimization: analysis, convex analysis, linear algebra, analysis of algorithms, complexity.
Languages: written and spoken English, French is optional.
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 (90 days / year) and flexible organization of working hours
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage under conditions
Remuneration
2200 euros gross salary /month
General Information
- Theme/Domain :
Optimization, machine learning and statistical methods
Scientific computing (BAP E) - Town/city : Saint-Martin-d'Hères
- Inria Center : Centre Inria de l'Université Grenoble Alpes
- Starting date : 2026-10-01
- Duration of contract : 3 years
- Deadline to apply : 2026-08-26
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
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
-
PhD Supervisor :
Besancon Mathieu / mathieu.besancon@inria.fr
The keys to success
Mathematical rigor
Scientific curiosity
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.