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Field
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thinking and a deep interest in the relationship between storytelling, popular culture and social change. You will be confident designing and managing your own research project, communicating complex ideas
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Stephen & Denise Adams Center for Parkinson's Disease at Yale School of Medicine | New Haven, Connecticut | United States | about 2 months ago
-generation AI models of gene regulation and disease progression. Our work is redefining Parkinson’s disease biology and enabling translational breakthroughs. The role Develop deep learning models across genome
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, logistics management, machine learning, deep learning, and optimization; Proficient in written and spoken English - essential for data analysis and communication with stakeholders Interpersonal skills with
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. Programming experience in Python, Matlab, and SQL. Mathematical modeling and data analysis. Machine/Deep learning (e.g. Pytorch, TensorFlow, JAX). City of Hope employees pay is based on the following criteria
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systems or proof-of-concept demos. • Design, code, and optimize robust deep learning pipelines • Project reports writing and presentation. Qualifications • Possess a Bachelor degree or above in
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-Hope Team. This paid position involves working alongside our Relational Care Fellow to build out the Resilient-Hope Initiative—a framework designed to study, teach, and operationalize resilient-hope as a
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neurobiological basis of intelligence. Research and development of new AI or ML methodologies or models from an engineering perspective, as well as scientific studies focused on understanding deep learning
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. Willingness to support project reporting and milestone reviews. Hard skills Strong programming skills in Python and deep learning frameworks such as PyTorch or TensorFlow. Experience in autonomous navigation
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About the Role The Centre for Epidemic Response & Modelling (CERM) at NUS Saw Swee Hock School of Public Health seeks a Research Fellow with deep expertise in Bayesian statistical modelling and
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for: Conducting research and development on AI-based solutions for automated defect inspection and condition assessment of train components by designing and developing deep learning, computer vision, and machine