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Field
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Skills Ability to design and implement a rigorous experimental scientific protocol Solid foundation in Python programming and an understanding of AI architectures based on LLMs and VLMs Knowledge of visual
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hold a Ph.D. in materials physics or scientific imaging, with proven experience in spectral imaging techniques (e.g., software such as Python, R, HyperSpy) and/or synchrotron techniques (luminescence, UV
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(PIT-tags, hydrophones, geophones, etc.). 2) Modeling and data analysis: - Proficiency in GIS tools (QGIS, ArcGIS) and hydraulic modeling software (HEC-RAS, TELEMAC, Mike). - Advanced programming (Python
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proficient in written and spoken English. Good programming skills (e.g. Python, R, or MATLAB) and previous experience with EEG/fNIRS/fMRI/eye-tracking/computational modelling are desirable. If any student in
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Experience1 - 4 Additional Information Eligibility criteria - PhD on steganalysis and/or watermarking - knowledge in image coding and on generative methods - knowledge about python Additional comments NA
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). Additional skills: Written and spoken English; Python programming. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5129-MARCLO-110/Default.aspx Work Location(s) Number of offers
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numerical simulations and analytical calculations, (iii) data analysis by programming, e.g. python and matlab, (iv) help and guide of Ph.D students in the team, (v) writing research reports, publishing
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, particularly EEG; • Programming experience, particularly in R and Python; • Proficiency in statistical methods commonly used in psychology and neuroscience (ANOVA, linear models, etc.); • Familiarity with
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, statistics and scientific computing. * Proficiency in a scientific programming language, particularly Python, and the ability to develop reproducible processing procedures. * Knowledge of machine learning and
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bioinformatics, computational biology, genomics, evolutionary biology or a related discipline. Essential skills include experience with genomic data analysis, proficiency in Python and/or R, familiarity with