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[map ] Subject Areas: Mathematics, AI-based drug design and discovery, Bioinformatics/Protein Engineering/Single-cell Omics Data, Mathematical AI/Machine Learning/Deep Learning, and Computational
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to diverse academic and industrial audiences. Proficiency in Python and deep learning frameworks such as PyTorch. Experience with Linux environments and GPU cluster management is essential. Competent in
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leader • Excellent written and oral communication skills Preferred Qualifications • Background in antisemitism studies • Experience with R, NLP and deep learning libraries • High performance computing
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Skills :Programming, Research Knowledge :IA, Deep Learning, Python Where to apply Website https://rubis.univ-spn.fr/offres/voir/206 Requirements Specific Requirements Be a graduate of an Engineering
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stakeholder groups. Deep understanding of business processes and IT alignment, with the ability to deliver measurable value. Skilled in resource planning and budgeting for large-scale projects. Self-motivated
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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structures and corresponding images) needed for training and validating deep learning (DL) models. Work closely with members of the ICMN nanostructures group or external collaborators. Communicate research
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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for candidates appointed as lecturers to teach online courses exclusively. For more information, please visit https://www.bu.edu/eng/academics/departments-and-divisions/electrical-and-computer-engineering/current
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the measurement instrument in close collaboration with our industrial partner, Veridis Technologies. An ideal candidate has experience in vibrational spectroscopy and spectral processing. Expertise in deep learning