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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # FPGA Accelerated Near-Storage Data Analytics - **Company:** Inria - **Location:** Rennes, France (Remote available) - **Salary:** €27,600.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Big Data, Data Centers, Data Distribution Service, Distributed Systems, Middleware, Hardware Design, Machine Learning, Objective-C (Programming Language), Scientific Computating, Data Streaming, Storage Devices, Containerization, Kubernetes, Storage Technologies, Enterprise Integration, Docker - **Published:** September 8, 2026 - **Apply:** https://jobs.inria.fr/public/classic/fr/offres/2026-10415 ## About the Role * An excellent academic record in computer science courses * Knowledge on distributed systems and data management systems * Strong programming skills (Python, C/C++) * Familiarity with containerized environments (e.g., Docker, Podman, Apptainer, Kubernetes) * Ability and motivation to conduct high-quality research, including publishing the results in relevant venues * Very good communication skills in oral and written English * Open-mindedness, strong integration skills and team spirit Appreciated: * Knowledge on machine learning and data analysis methods * Professional experience in the areas of HPC and Big Data management ## Description Computational storage [15, 16] is a promising technology to improve the efficiency and performance of various workloads, for example in scientific computing, machine learning (ML), and artificial intelligence (AI). As the volume of generated data continues to grow exponentially [5,9] conventional compute and storage architectures are increasingly constrained by large data movements between storage/memory and compute resources. Computational storage can eliminate many of these data movements by co-locating compute capabilities along with storage locations allowing to offload the typically much smaller (sub)programs [1, 12, 14]. While the concept is well established in literature, computational storage devices are not widely commercially available or deployed in data centers today. A key challenge is that computational storage encourages domain-specialization for highest efficiency while economic factors encourage commoditization of products catering to broad markets. A second challenge is that both for legacy applications as well as emerging applications such as ML/AI it remains an open research question how to program and orchestrate across distributed platforms with computational storage capabilities. Recent advancements in programming models and software portability on the one hand, and reconfigurable hardware and domain-specific hardware design on the other [5, 7, 11], suggest that a modular approach that identifies common building blocks across domain boundaries might hold the key to both aforementioned challenges. Computational storage research exists on accelerating specific workloads or applications [5, 11, 14] as well as on emulating computational storage devices but a systematic study focusing on scientific computing workloads and modelling of suitable architectures and data distribution strategies is missing. Research Objectives This project aims to advance the research on computational storage for scientific computing and artificial intelligence applications. It will investigate mechanisms to formalize, capture, model and evaluate computational storage in distributed environments. The project is structured into three primary objectives: * Objective A: realize a survey and define ontologies and taxonomies for distributed computational storage systems from multiple angles across multiple domains: the application perspective (programming paradigm, data flows), the middleware perspective (workflow orchestration, resource management and data placement), and the system perspective (e.g., hardware, storage, compute) * Objective B: establish the technical foundations to empirically assess hardware performance and decompose existing workflows and task characteristics on the one hand, as well as implement suitable computational storage testbeds (e.g. simulated [2,3,4], emulated [7, 10], and on realistic software stacks and physical hardware [6, 8, 9, 11]) * Objective C: develop a methodology to faithfully model performance extrapolations to large-scale deployment scenarios as a means to identify and validate common computational storage building blocks ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Developer’s Perspective: Overview of the Tezos Blockchain Ecosystem](https://www.wearedevelopers.com/videos/237-developer-s-perspective-overview-of-the-tezos-blockchain-ecosystem) - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) ## Related Articles - [Top 6 Hackathons for Developers in 2023](https://www.wearedevelopers.com/magazine/263-top-6-hackathons-for-developers-in-2023) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Building AI Solutions with Rust and Docker](https://www.wearedevelopers.com/magazine/494-building-ai-solutions-with-rust-and-docker) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Résumé-Driven Development: How IT trends affect the job market for software developers](https://www.wearedevelopers.com/magazine/59-resume-driven-development-how-it-trends-affect-the-job-market-for-software-developers) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)