17 Feb 2024
Job Information
- Organisation/Company
CNRS- Department
Laboratoire d'Informatique de Grenoble- Research Field
Computer science
Mathematics » Algorithms- Researcher Profile
First Stage Researcher (R1)- Country
France- Application Deadline
8 Mar 2024 - 23:59 (UTC)- Type of Contract
Temporary- Job Status
Full-time- Hours Per Week
35- Offer Starting Date
1 May 2024- Is the job funded through the EU Research Framework Programme?
Not funded by an EU programme- Is the Job related to staff position within a Research Infrastructure?
No
Offer Description
Location: POLARIS team (https://team.inria.fr/polaris/ ), a joint research team between CNRS, Inria and Université Grenoble Alpes, part of the LIG Laboratory, a mixed research unit (UMR5217) of the CNRS (450 people). The team is located on the Saint Martin d'Hères campus, accessible by tram from Grenoble.
Other benefits:
- 45 days annual leave
- Partial coverage of complementary health insurance costs.
- Subsidized catering available on campus
- Partial reimbursement of public transport costs
From automated hospital admission systems powered by machine learning (ML), to flexible chatbots capable of fluent conversations and self-driving cars, the wildfire spread of artificial intelligence (AI) has brought to the forefront a crucial question with far-reaching ramifications for the society at large: Can ML systems and models be relied upon to provide trustworthy output in high-stakes, mission-critical environments?
These questions invariably revolve around the notion of robustness, an operational desideratum that has eluded the field since its nascent stages. One of the main reasons for this is the fact that ML models and systems are typically data-hungry and highly sensitive to their training input, so they tend to be brittle, narrow-scoped, and unable to adapt to situations that go beyond their training envelope. On that account, robustness cannot be achieved by blindly throwing more data and computing power to larger and larger models with exponentially growing energy requirements (and a commensurate carbon footprint to boot). Instead, this thesis proposal intends to focus on the core theoretical and methodological foundations of robustness required for current and emerging AI systems.
In more detail, to address the challenges that arise when ML models and algorithms are deployed and interact with each other in real-life environments, we plan to develop the required theoretical and technical tools for AI systems that are able to (a) adapt “on the fly” to non-stationary environments; and (b) gracefully interpolate from best- to worst-case guarantees. In particular, this thesis intends to focus on the replicator dynamics, a particularly fruitful model of multi-agent learning in games, the overarching objective being to obtain a complete description of its robustness to noise and uncertainty. Familiarity and experience with the replicator dynamics will be a prerequisite for this PhD.
Requirements
- Research Field
- Computer science
- Education Level
- PhD or equivalent
- Research Field
- Mathematics
- Education Level
- PhD or equivalent
- Languages
- FRENCH
- Level
- Basic
- Research Field
- Computer science
- Years of Research Experience
- None
- Research Field
- Mathematics » Algorithms
- Years of Research Experience
- None
Additional Information
- Website for additional job details
https://emploi.cnrs.fr/Offres/Doctorant/UMR5217-PANMER-004/Default.aspx
Work Location(s)
- Number of offers available
- 1
- Company/Institute
- Laboratoire d'Informatique de Grenoble
- Country
- France
- City
- ST MARTIN D HERES
- Geofield
Where to apply
- Website
https://emploi.cnrs.fr/Candidat/Offre/UMR5217-PANMER-004/Candidater.aspx
Contact
- City
ST MARTIN D HERES
STATUS: EXPIRED