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deep learning, optimisation, sampling. Good coding skills for numerical simulation (Pytorch, Python, MATLAB, ...). A general interest in health and biology is welcome. Practical information MORPHEME
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programs that demonstrate impact and accountability. Your efforts will acquire and retain alumni, staff, corporate partners, and community donors through strategic marketing approaches and compelling grant
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Want a career with a deep sense of purpose while working with the support of a highly skilled, dedicated and compassionate interdisciplinary team? Utilize and learn skilled nursing care and see the
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guidance and academic support for students. Demonstrating enthusiasm for teaching, the teaching/learning process and for individual students. Applying your skills and experience to your assigned teaching
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support for students. Demonstrating enthusiasm for teaching, the teaching/learning process and for individual students. Applying your skills and experience to your assigned teaching schedule. Participating
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. Experience in deep learning and image analysis is strongly preferred. Qualifications Required Qualifications: PhD in biotechnology, biomedical engineering, or related area, with one or more years of
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Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
. This dataset will enable the training of specialized deep learning models (neural network or transformer) for automated segmentation of tibial plateau fractures. iii) The algorithm must then be trained
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Education: Ph.D. or M.S. in Computer Science, AI, Computer Vision, or related field Experience: 3+ years in computer vision and deep learning, with specific focus on microscopic imaging, generation
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academic support for students. Demonstrating enthusiasm for teaching, the teaching/learning process and for individual students. Applying your skills and experience to your assigned teaching schedule
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scripting methods (i.e. Python, MATLAB, C++, CUDA, Bash, and/or SQL) and machine learning / deep learning methods Active learning, exploration, optimal experiment design, Bayesian optimization, reinforcement