936 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" Fellowship positions
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laboratory scientists, computational biologists, and clinical academic colleagues, running computer simulations and testing predictions in the lab. About you You should be highly motivated, and ambitious
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and the associated vulnerability patterns in aging and neuropsychiatric disorders using multimodal neuroimaging and machine learning methods. We are interested in the large-scale brain structural and
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microscopy systems that integrate machine learning, robotic control, and real-time data analysis to achieve autonomous imaging and interpretation of complex materials systems. The Fellow will design and
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Full-time: 35 hours per week Fixed term: 6 months Develop machine learning of cellular automata models with applications to in vitro developmental biology. The Opportunity: This post is full-time
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phenotyping using both drone-based and ground based sensing platforms. Learn artificial intelligence and machine learning techniques to analyze image and geospatial data from diverse sources for crop monitoring
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regions will be evaluated for features such as signatures of selection or diversifying or purifying selection, around genes and regions of agricultural importance. Learning Objectives: The participant will
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holder must have: Expertise in advanced theoretical and computational electromagnetics, including high frequency asymptotics. Expertise in artificial intelligence and machine learning. Recent research
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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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instruments Ability to occasionally lift and move laboratory supplies or equipment weighing up to approximately 25 pounds Ability to work at a computer workstation for data analysis and manuscript preparation
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to, teaching, medicine, law, engineering, nursing, social work, the police, and the military. Both quantitative and qualitative approaches would be relevant, and comparative approaches (cross-sector, cross