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Field
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techniques (geomorphic mapping, TruPulse, DGPS/drone surveys), engineering geology methodologies (slope stability, rock strength assessment), coding (python/matlab data analysis and modelling) and transferable
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programming (e.g., Python, MATLAB). Energy system modelling expertise with experience in academic research Preferred Skills: Educational background in Electrical Engineering, Computer Science, Renewable Energy
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are welcome to apply. You have strong programming skills in Python and/or MATLAB and/or R. You have experience with EEG, MEG, or related neuroimaging methods, time-frequency analyses, or a strong interest in
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equipment (e.g., 3D scanning, surface inspection). Familiarity with data analysis tools (e.g., MATLAB, Python, or similar) and basic knowledge of machine learning is an advantage. Familiarity with research
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geophysics, physics, applied mathematics, or computer science will be considered. Expertise in high-level programming language like MATLAB or Python is essential. Where to apply Website https://emploi.cnrs.fr
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and programming (MATLAB, Python, R, or other scientific coding/programming). Affinity with computational neuroscience, connectomics and/or Machine Learning is appreciated. Our goal perform connectome
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with emerging memory devices Experience with simulation tools (LTspice, Cadence, MATLAB, Python) Interest in brain-inspired computation, energy-efficient hardware, and experimental validation Ability
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selection criteria Experience in laboratory work, preferably related to chemical absorption Experience in modelling using Matlab or Python You have knowledge of the chemistry of ionic solutions and are
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background in applied mathematics, computational mathematics, computer science, physics, or engineering is suitable. Basic programming experience (e.g., C, C++, Julia, MATLAB, Python, or similar) is necessary
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environment Proven ability to use a scientific programming language such as python or MATLAB for signal processing A desire to improve therapies available to patients with neurological conditions Excellent