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, granularity, semantics, attributes) using QGIS and Python for data exploration and preprocessing - designing an approach for automatic change detection using AI techniques (classification, clustering
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experimental data acquisition system (LabView, etc.) and programming skills (Python, etc.) for data analysis would be a plus. Familiarity of cryogenics is not necessary. Intrinsic orientation for research and
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to power and energy systems • Proficiency in Python, with experience using optimization frameworks such as Pyomo or Linopy and optimization solvers • Knowledge of advanced optimization and decomposition
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, or Mining Engineering, with demonstrated link/application to the mineral raw materials sector. The candidates should demonstrate some of the following skills: - coding in Python - Life Cycle Thinking (LCA
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programming tools (Matlab, Python, C++) is essential, and knowledge of NMR/MRI would be a valuable asset. Mechanistic insights and optimization of synchronized respiratory stimulation as a therapeutic
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, or laser instrumentation is appreciated - programming skills for data acquisition and analysis (Python) - interest in quantum technologies and solid-state quantum emitters. - strong motivation
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Linux and a programming language (C/C++ or Python) is a plus but not mandatory. Location: PROMES CNRS Laboratory – Perpignan site Starting date: April 1st , 2026 at the latest Salary: Minimum gross
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., using Python). - Strong interest in research and modeling approaches. - Ability to work in a team at LSCE, IPSL, and within the AMACLIM project. - Synthesis skills (literature review, analysis of model
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Inria, the French national research institute for the digital sciences | Sophia Antipolis, Provence Alpes Cote d Azur | France | 3 months ago
++, CUDA, Python, and PyTorch LanguagesFRENCHLevelBasic LanguagesENGLISHLevelGood Additional Information Benefits Subsidized meals Partial reimbursement of public transport costs Leave: 7 weeks of annual
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ecology. A strong quantitative mindset is essential, including good skills in data analysis using R, Python or similar tools. Experience with trophic ecology and Bayesian approaches would be an advantage