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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
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and scientific computing. - Ability to formulate and conduct a research project independently, combining mathematical approaches, physical interpretation and numerical validation. - Proficiency in at
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. Previous experience in detector characterization or testing with particle beams would be a strong asset. • Have good programming skills, with experience in languages and scientific computing tools such as C
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interdisciplinary work and team collaboration. Skills or experiences in the following areas will be considered as a plus : - Physical modeling and simulation; -Scientific computing and partial differential
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security, non-destructive testing, infrared imaging and cultural heritage analysis. SICOIA offers a stimulating scientific environment with access to high-performance computing resources within the Paris