PhD on dynamic selection of black-box optimization algorithms (M/F/D)
New
- FTC PhD student / Offer for thesis
- 36 mounth
- BAC+5
This offer is available in English version
This offer is open to people with a document recognizing their status as a disabled worker.
Offer at a glance
The Unit
LIP6
Contract Type
FTC PhD student / Offer for thesis
Working hHours
Full Time
Workplace
75252 PARIS 05
Contract Duration
36 mounth
Date of Hire
01/05/2026
Remuneration
minum of 2300 € gross monthly
Apply Application Deadline : 20 April 2026 23:59
Job Description
Thesis Subject
Description of the thesis topic
Black-box optimization algorithms are general-purpose solvers. They work in an iterative fashion, alternating between generating solution candidates, evaluating them, and adjusting the strategy by which the next candidates are generated. Black-box optimization algorithms do not need access to an explicit problem formulation nor to instance data; it suffices that the quality of the solution candidates can be assessed externally, e.g., via numerical simulations or physical experiments. This feature makes black-box optimization algorithms particularly useful for broad ranges of optimization problems where limited time or knowledge are available to develop problem-tailored solution strategies.
A plethora of black-box optimization algorithms exist, complementing each other in strengths and weaknesses for different problem types and for different stages of the optimization process. While this complementarity is widely acknowledged, we lack efficient approaches to leverage it, resulting in sub-optimal solutions that cause an ineffective use of our limited resources. With the dynaBBO project, funded via an ERC Consolidator grant, we set out to fill this important gap. Relying on a hybrid approach synergizing knowledge about black-box optimization algorithms with automated machine learning techniques, we obtain an efficient system, capable of dynamically switching between different black-box optimization algorithms ``on the fly''.
For the evaluation of different AutoML techniques, and to gain the right intuition for fine-tuning our approaches, we will derive examples with provably optimal selection strategies. Developing these examples is the purpose of the proposed PhD thesis.
Your Work Environment
The thesis will be carried out at the LIP6 Computer Science department of Sorbonne Université. The student will be supervised by CNRS research director Carola Doerr. The student will integrate into the Operations Research team (RO) of LIP6.
The PhD thesis is funded via the ERC Consolidator grant “dynaBBO: Dynamic Selection and Configuration of Black-box Optimization Algorithms”. Travel funding and resources for a research stay of up three months with one of our international collaborators is available. The PhD student will have access to the computing facilities of the LIP6 Computer Science lab and of Sorbonne University.
Our working language is English. No French skills are required.
Compensation and benefits
Compensation
minum of 2300 € gross monthly
Annual leave and RTT
44 jours
Remote Working practice and compensation
Pratique et indemnisation du TT
Transport
Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€
About the offer
| Offer reference | UMR7606-CARDOE-010 |
|---|---|
| CN Section(s) / Research Area | Information sciences: bases of information technology, calculations, algorithms, representations, uses |
About the CNRS
The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.
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