1,022 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" Fellowship positions
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should also have an interest in data science, machine learning tools or artificial intelligence as applied to biological systems related to immune therapy of cancer. The postholder should be committed
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candidates/candidates who are in the closing stages of their master’s degree can also apply Solid background in artificial intelligence and machine learning, including deep neural networks Programming
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-inspired learning algorithms for efficient, robust and scalable pattern recognition; (b) assist in general management of the project; and (c) perform any other duties as assigned by the project leader
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Digital Twin Framework for Smart and Sustainable Advanced Manufacturing Research area 3: Advanced Multifunctional Materials The ideal candidates would have a background in machine learning, manufacturing
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are offered an opportunity for an independent research project using lab data to gain experience conducting ecological data analysis, manuscript writing, and publishing in peer-reviewed journals. Learning
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radiochemistry, computational chemistry, and/or nuclear materials science. Preferred Qualifications Depending on the project, experience with Python, machine learning techniques, and any of the following tools is
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, you will apply machine learning (ML) methods to discover reduced-order models from data and develop GenAI-based techniques for generating high-resolution climate projections. In addition to developing
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, including survival analysis, time-series techniques, causal inference approaches, and/or machine learning methods to large healthcare datasets. Prior experience mentoring or supervising graduate students
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-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI approaches to biological questions Collaborating closely with
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pathogens such as Japanese encephalitis and Rift Valley fever. Learning Objectives: The fellow will learn epidemiological techniques related to modeling parasitic and vector-borne diseases. Opportunities