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project activities and writing reports Your Profile MSc or PhD in a relevant field Experience with Machine Learning modeling Coding experience, preferably Python Basic knowledge of databases tools and
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of Python programming and machine learning tools, deep learning, and large language models is desirable. The fellow will be based at the Engineering College of the São Paulo State University (UNESP), in its
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knowledge in at least of the following techniques : EEG, MRI, TMS • Good programming skills (python, R, matlab). • Knowledge of legal and deontological frameworks • English : B1 to B2 Know-how • Organise
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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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, 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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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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models, statistical analysis). Proficiency in programming languages (e.g., R, Python, MATLAB) and familiarity with relevant air quality modeling tools. Experience with large datasets and data processing
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multispectral/hyperspectral data processing. Proficiency in programming languages such as Python or R for data analysis and processing. Excellent communication skills and the ability to work effectively in a