ofertas na Cnrs em França

Organisation/Company CNRS Department Maison de la Simulation Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage Researcher (R1) Country France Application Deadline 26 Dec 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Feb 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

With the increasing complexity of numerical simulation codes, new approaches are required to analyze the ever‑growing amount of data. This requires coupling up‑to‑date data analysis libraries with the existing highly optimized numerical simulation codes. The PDI Data Interface code coupling library is designed to fulfill this goal.

The open‑source PDI Data Interface library is designed and developed for process‑local loose coupling in high‑performance simulation codes. PDI supports the modularization of codes by inter‑mediating data exchange between the main simulation code and independent modules (plugins) based on various libraries. It is developed in modern C++ and offers C, Fortran, and Python API.

PDI offers a reference system similar to Python or C++'s shared_ptr with locking to ensure coherent access by coupled modules. It provides a global namespace (the data store) to share references and implements the Observer pattern, enabling modules to react to data availability and modifications. It implements a metadata system that can specify a dynamic type for references based on the value of other data (e.g., array size based on the value of a shared integer). Codes using PDI's declarative API expose the buffers in which they store data and trigger notifications when significant steps in the simulation are reached. Third‑party libraries such as HDF5, JSON, or netCDF are wrapped in a PDI plugin. A YAML configuration file is used to interleave plugins and additional code without modifying the original application.

Another aspect we explore with PDI is in‑situ data analysis, which performs numerical analytics during the simulation. This is necessary due to the ever‑growing gap between file system bandwidth and compute capacities. To this end, we are developing the Deisa plugin. This plugin is based on the open‑source Dask framework and enables us to transfer data to dedicated processes for in‑situ analysis.

One of our goals is to establish a feedback mechanism between the in‑situ data analysis and the numerical simulation. This allows better resource allocations and on‑the‑fly simulation monitoring. Another aspect that in‑situ analysis enables is the use of AI methods for HPC and HPDA. For instance, we can employ unsupervised detection of rare events during the simulation, which can significantly reduce the amount of data produced, thereby reducing stress on the file system.

As a member of the newly created PDI team, your primary focus will be on developing and maintaining the PDI library.

• Develop core functionalities and new plugins for PDI
• Develop the Deisa library
• User‑support
• Organize training sessions
• Library packaging and deployment

At the Maison de la Simulation laboratory, you will join a group of engineers and scientists focusing on all aspects of high‑performance computing (HPC). You will have the opportunity to collaborate with PDI users and to introduce new features in the PDI plugin family. As a member of the PDI team, you will also have the opportunity to exchange with the developers of other HPC codes to enrich your skills in HPC code development. To validate your developments, you will be provided with access to the top European supercomputers (Adastra, Jean‑Zay, etc.).

The successful candidate will master the following skills and knowledge:

• Proficiency in modern C++ (C++14 and above)
• Software engineering and library design
• Modern development environment (Linux, git, CMake, etc.)
• Communication (writing, presenting, and training)
• Team‑work and integration in an international environment

In addition, the following will be considered a plus:

• Data analysis libraries such as Dask
• Knowledge and experience with Python, Fortran and/or GPU computing
• HPC and parallel libraries such as OpenMP and MPI
• HPC parallel IO libraries such as HDF5 or NetCDF
• Experience with supercomputer tools (slurm, sbatch, etc), packaging and deployment