PhD Position F/M Frugal Distributed Training with Volatile Resources
Inria
Inria is recruiting a PhD researcher to develop frugal distributed-training algorithms for volatile and resource-constrained computing environments.
The current cycle is confirmed, the official application route is active and a closing date is recorded.
Source visited 21 Aug 2026 (UTC). A source visit does not establish every employment term or confirm that applications remain open.
- Source review was due 24/08/2026. Confirm details on the official page.
- Reviewed evidence is not available for: deadline, employment terms, requirements, application link, compensation.
Vacancy terms
- Career stage
- Doctoral researcher
- Employment type
- Not stated
- Contract
- Not stated
- Contract ends
- Not stated
- Original contract term
- Fixed-term PhD contract for three years
- Schedule
- Not stated
- Workplace
- Not stated
- FTE
- Not stated
- Salary
- EUR 2,300 gross per month.
- Department or school
- Not stated
- Posted
- Not stated
Responsibilities
- Model volatile resource availability and design distributed-training algorithms.
- Analyse convergence, communication, computation and energy trade-offs and evaluate methods experimentally.
Qualifications and requirements
Qualifications
- Graduate degree or equivalent.
- Solid mathematical background in optimization, probability, stochastic processes or statistical machine learning.
- Good programming skills, preferably Python.
- Fluency in English.
Requirements
- Strong interest in distributed systems and resource-aware machine learning.
- Interest in theoretical analysis and experimental validation.
Documents and steps
Documents
A required-document list has not been captured. Confirm the current checklist on the official application page.
Steps
Review the distributed-training research objectives.
Prepare the application materials requested by Inria.
Submit online through the official Inria vacancy by 25 October 2026.
Sources behind this vacancy
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Published revision history
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PhD Position F/M Frugal Distributed Training with Volatile Resources
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Programme overview and cycle history