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Post-Doctoral Research Visit F/M Event detection in Tokamak simulations
Il y a 2 mois
The Inria Saclay-Île-de-France Research Centre was established in 2008. It has developed as part of the Saclay site in partnership with Paris-Saclay University and with the Institut Polytechnique de Paris since 2021.
The centre has 39 project teams , 27 of which operate jointly with Paris-Saclay University and the Institut Polytechnique de Paris. Its activities occupy over 600 scientists and research and innovation support staff, including 54 different nationalities.
Contexte et atouts du poste
While artificial intelligence is growing at a fast pace, the bulk of the world's computing power remains targeted at modeling and predicting physical phenomena, such as climate models, weather forecasting, or nuclear physics. These simulations are run on highly parallel supercomputers on which both the hardware and the software are optimized for the task at hand. While the computing power of each processing unit is still increasing, the communication networks and the storage capabilities in these clusters do not follow such fast trends. As a result, computing nodes produce outputs faster than what can be stored or sent to process elsewhere: These simulations are IO-bound.
To reduce the communication burden, a promising venue is in situ computations, meaning that most of the data is processed locally by the nodes, and only meaningful aggregates are stored or sent over the network. However, this is a difficult problem in general since, to detect meaningful information for the global simulation, one often needs to detect particular events, requiring non-local information that depends on the other nodes' output. The goal of this project is to leverage machine learning techniques to detect local events in tokamak simulations and help steer follow up simulations optimally. This work is thus placed in a broader "Machine Learning for Science" context, which aims at using ML to solve key problems arising in traditional sciences.
Environment. The postdoc will take place in Inria Saclay, in the MIND team. This is a large team working focused on mathematical methods for statistical modeling of brain function using neuroimaging data (fMRI, MEG, EEG). Particular topics of interest include machine learning techniques, numerical and parallel optimization, applications to human cognitive neuroscience, event detection, and scientific software development. A particular emphasis is put on interdisciplinary projects.
Mission confiée
This project intends to provide efficient statistical modeling tools for processing tokamak simulation outputs. The goal of these tools will be to help identify anomalies or errors in the data, which can indicate problems with the HPC system or the underlying algorithms. This information can then be used to adjust the computations and ensure that the HPC system is running smoothly and efficiently. Moreover, statistical processing can also be used to analyze the outputs and detect events of interest to select the right snapshots of the simulation to allow post-processing of the results. By properly selecting the information to save, we can ensure that the HPC system is able to handle large volumes of output without running into storage or bandwidth limitations.
As specified above, the project will first focus on event and anomaly detection techniques for tokamak simulation. The main objective of this post will be to setup an environment to evaluate event detection algorithms to process tokamak simulation outputs.
Principales activités
Main activities :
- Read papers and state of the art
- Benchmark existing algorithms
- Adapt the formulation to the target scenario.
- Program, run, and analyze simulation results.
Complementary activities
- Participate to the teams activities : scientific meetings, seminars, scientific presentations.
Compétences
- Strong mathematical background. Knowledge in machine learning is a plus.
- Good programming skills in Python. Knowledge of a deep learning framework is a plus.
- The candidate should be proficient in English. Knowing French is not necessary, as daily communication in the team is mostly in English due to the strong international environment.
Avantages
- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage
Rémunération
2788 € gross/month
