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Field
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candidates with a proven track record in developing open-source machine learning, deep learning, or cheminformatics tools (Preferred written in Python). Job Duties Plans, directs and conducts research
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-level optimisation using TRNSYS and MATLAB/Python. The associate will use advanced characterisation (e.g., DVS, porosimeter, DSC/LFA) to develop and assess adsorption composites, and apply CFD to analyse
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. Proficiency in Python programming and major ML/DL frameworks (e.g., PyTorch, TensorFlow). Solid understanding of optimization and regularization methods for training complex neural networks. Practical knowledge
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Python, using GPUs), with the precise balance determined by the candidate’s background and interests. The mathematical tools involved will include matrix analysis, optimization, backward error analysis
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architecture. Programming skills in Python, ROS, TensorFlow, or MATLAB. Strong interest in applied AI and autonomous systems development. Application materials: CV, cover letter, and one recommendation letter
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source level with Python and MNE-Python - Pipeline development in Python and MNE-Python - Articles preparation, presentations and workshopsmantenimento di codice open source. The position is hosted
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, engineering, computational science, a physical science (materials science, chemistry, physics etc.), or related field. Hands-on experience with AI frameworks and employing large language models. Strong Python
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the following: Conduct research in the area of aging, balance, fall rehabilitation, app development, and biomechanics. Data Post-processing. Programming using MATLAB, Python, and Java/Kotlin (for Android
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the preparation of technology transfer and valorization steps. • Software development (Python, PsychoPy, real-time analysis and visualization tools). • Integration of acquisition modules (EEG, fMRI
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Eligibility criteria - Solid background in fluid mechanics - Expertise in Python - Oral and written communication in English - Interest in interdisciplinary environment Website for additional job details https