PhD in Computational Storage for Scientific Computing | Flexible Hours

Inria; Rennes; Sur place; EUR 17 000 - 21 000; Plein temps

Avantages offerts par ce poste

Public transport refund

Leave: 7 weeks + RTT days

Teleworking after 6 months

Equipment provided

Social, cultural and sports events

Résumé du poste

Inria is seeking a PhD candidate to join the KerData team in Rennes. The project focuses on computational storage and its applications to scientific computing and AI, with collaborations across European partners.

You will pursue three objectives: survey ontologies, build empirical testbeds, and model performance for large-scale deployments. The position offers modern research facilities and close mentoring by senior researchers, with opportunities to publish in top venues and contribute to open

Qualifications

• Excellent academic record in computer science.
• Knowledge on distributed systems and data management.
• Familiarity with containerized environments (Docker, Podman, Apptainer, Kubernetes).
• Strong motivation to conduct high‑quality research and publish.
• Excellent English communication skills, both written and oral.

Responsabilités

• Carry out doctoral research on computational storage for scientific computing.
• Develop ontologies, taxonomies and modeling methods for distributed storage systems.
• Collaborate with international partners and within the KerData team.
• Prototype software and run experiments on HPC platforms and datasets.

Connaissances

Academic excellence in CS

Distributed systems

Data management systems

Containerized environments

Research communication (English)

Team collaboration

Formation

PhD in Computer Science

Outils

Docker

Podman

Apptainer

Kubernetes

Description du poste

Inria is seeking a PhD candidate to join the KerData team in Rennes. The project focuses on computational storage and its applications to scientific computing and AI, with collaborations across European partners.

You will pursue three objectives: survey ontologies, build empirical testbeds, and model performance for large-scale deployments. The position offers modern research facilities and close mentoring by senior researchers, with opportunities to publish in top venues and contribute to open