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developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and applications of AI. Proficiency in Python, leading ML frameworks
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applications of AI. Proficiency in Python, leading ML frameworks (e.g., PyTorch, TensorFlow, JAX), and scientific libraries (e.g., NumPy, SciPy, scikit-learn) Familiarity with medical images such as x-ray, CT
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, computer science (medical image processing), medical physics. Computer skills: Python, C++ (ITK, RTK). Languages: English required, French optional. Website for additional job details https://emploi.cnrs.fr/Offres
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understanding, or digital twins • Experience working with real-world and synthetic datasets and sensors (e.g., RGB, depth, LiDAR) • Proficiency in Python and frameworks such as PyTorch, OpenCV, or Open3D • US
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are also encouraged to apply. Experience coding for neural data analysis using Matlab or Python is helpful. See colonneselab.org for more information. Duties include: 1. Design and implementation of research
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(or a related field), is proficient in both mechanistic modeling and ML frameworks (e.g., TensorFlow, PyTorch), and has strong programming skills (Python/MATLAB/C++). Join us to tackle sustainability
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Duties Designs and conducts histology and imaging analysis in animal models. Uses computational tools (R, Python, BLAST) for data analysis. Writes research papers for peer-reviewed journals. Presents
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, Psychology, or a related field. strong background in computational modelling and/or functional neuroimaging techniques. proficiency in programming languages commonly used in neuroscience research (e.g., Python
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computational skills, with experience in advanced data analysis and statistical methods Proficiency in computer programming, ideally in MATLAB, Python or similar tools for scientific data analysis and
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: Qualifications required: The candidate must have a PhD's degree in Biophysics, Biology, Bioengineering or equivalent. Expertise in image/data analysis and coding is required (Python, Image J). Experience in cell