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: The work plan addresses the needs in current Research and Development (R&D) projects in INESC TEC to build energy-efficient software prototypes for training deep learning models on large-scale
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the world’s largest supercomputers (Polaris, Aurora) and some of the most advanced characterization tools in the world at Argonne and Sandia National Labs. Candidates with a background in deep learning
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of physics- informed machine learning and deep learning, with applications to inverse problems in scientific imaging and the modeling of complex physical systems. The overall goal is to integrate the knowledge
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continuation should a business need warrant it. Essential Duties/Tasks Deep-learning/AI model training, UI/UX Design, and Game Development Train and test generative AI and reinforcement learning models Design
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purchasing policies, strategic sourcing goals, and operational standards. Success in this role requires both technical expertise and deep institutional knowledge of UCSF's supply chain processes, policies, and
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to develop and implement machine learning/deep learning tools for personalized medicine in cancer by exploiting electronic medical records and medical images in relation to cancer diagnosis and the
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of machine learning for healthcare and related topics Deep knowledge of multi-modal learning, transfer learning, foundation models, and self-supervised learning. Experience in dealing with large
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certificate, bachelors, and masters programs. The person in this role will also have a small teaching load to lead courses in ECE and the Montessori MAED programs. Responsibilities Include: Prepare and teach in
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learning libraries (e.g., PyTorch) Desirable criteria Research experience in one or more of the following areas: tactile sensing, robot grasping and manipulation, robot control, computer vision, deep
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. Expertise and knowledge in AI deep learning model development on histology whole slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and