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Field
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of organoids in 96-well plates R and Python programming from YAML in VS Code Statistical analyses using mixed models Personal Skills The candidate should demonstrate: A genuine service-oriented attitude, good
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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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, including intracranial EEG (iEEG/SEEG), local field potentials (LFPs) and single-unit recordings. The position has a strong focus on scientific software development in Python, data engineering and signal
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modelling (numerical simulations, e.g. matlab or python, or analytical modeling), OR optomechanics, OR high frequency devices. You are eager to learn and expand your knowledge! Basic knowledge of analog and
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their 4th or 5th year of studies (M1, M2 or gap year) - Computer vision skills - Machine learning skills (deep learning, perception models, generative AI…) - Python proficiency in a deep learning framework
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MEG or EEG), strong skills in artificial neural netwroks, strong programming skills (MATLAB and/or Python), and solid knowledge of statistical methods. Experience in designing, fitting and comparing
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French National Institute for Health Research (INSERM) | Lille, Nord Pas de Calais | France | 3 months ago
bioinformatics workflows in Python for the analysis of transcriptomic, proteomic, single-cell transcriptomic, and interactome datasets. Design and implement Big Data and machine learning approaches using Python, R
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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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of Python and R; Knowledge of web development (Shiny or equivalent technologies); Experience processing and analysing large datasets; Experience with version-control tools (Git); Data visualisation skills
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aerosols. - Visualization and post-processing tools: Proficiency in visualization tools (e.g., Python) and data post-processing tools to analyze simulation results and generate graphs, maps, and diagnostics