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recruited within ADACSI, offering co-supervision opportunities. The positions provide access to CNRS/LAPTh computing resources and generous travel support and, as an MIAI-funded chair, connect the
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algorithmic approaches and be conducted in the context of wireless networks, the Internet of Things (IoT), distributed learning, or edge computing infrastructures. The research topics may include, the
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used in combination with state of the art approaches for computing Gibbs free energies, such as thermodynamics integration or umbrella sampling. The atomic scale approach will also allow to obtain two
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frameworks such as JAX or PyTorch would be an advantage, as would experience with tools for cosmological simulation, Bayesian sampling or high-performance computing. Strong skills in Python programming and
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also benefit from the computational expertise and high-performance computing resources available within the host team. At the national level, the project is embedded in the collaborative ANR
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experience in designing and conducting experiments with human participants, as well as acquiring and analysing MEG and/or EEG data, is essential. • Excellent command of Python for scientific computing and
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and design of expression constructs. • Computing and modelling: familiarity with the Linux environment and with structural visualisation and analysis software (PyMOL, ChimeraX); a working knowledge
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. Experience with image analysis software (ImageJ/Fiji) and scientific computing tools (Python, R or equivalent) will be appreciated. Good command of spoken and written scientific English. Personal
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-performance computing resources for the LMDz-REPROBUS global model and data analysis. The doctoral student (M/F) will also benefit from 90 hours of training as well as logistical and technical support
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be an advantage. Familiarity with genomic data formats, high-performance computing, workflow managers such as Snakemake or Nextflow, and version control with Git would also be valuable, but prior