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Doctoral Researchers (PhD students) to work on deep learning methodologies for machine and robot perception. These positions are funded by the Horizon Europe project OPERA (Open Perception, Learning, and
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 1 month ago
General Summary of the Position Postdoctoral positions in Deep-Learning Omics are available in the Zhou Lab (https://profiles.umassmed.edu/display/20062865 ). The Zhou Lab at UMass Chan Medical
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multimodal data. Your responsibilities include: Developing and applying machine learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing
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modeling; pharmacogenomics; health informatics; data science, machine learning, or deep learning; causal inference methods Strong written and oral communication skills, with at least one first-author
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expected to learn basic R or python to generate plots and analyze data. Qualifications Requirements for employment are: You have deep knowledge of immunology including autoimmunity You have experience
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and deploy advanced deep learning and foundation models for surgical scene understanding segmentation, tracking, and operator assistance. You will write, test, and optimise Python and C++ code for real
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biomedical research. Your profile Master's degree in computer science or related discipline Experience with Python and recent deep learning frameworks (e.g. Pytorch, MONAI) Strong interest in image analysis
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware
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costs and energy requirements of state-of-the-art deep learning models significantly, while democratizing them for a vast community of users, researchers, and practitioners. The task is to perform just
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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation