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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 5 hours ago
modalities Developing and applying polygenic risk scores and causal inference models to predict disease onset, progression, and treatment response Implementing machine learning and statistical genetics
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learning or related fields; Proven experience in research work on AI models, in particular in the field of explainable AI, synthetic data generation or model testing; Proficiency in programming (preferably
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of heat transfer and turbulence physics in wall-bounded flows through numerical simulations, data-driven modelling, and machine learning techniques. Key goals include optimising convective heat transfer
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external partners. Topics of particular interest include the novel development and application of machine learning models--such as large language models, multi-modal foundation models, agentic AI, embodied
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on developing advanced machine learning models to quantify phenotypic traits of crops, including corn, soybean, and other selected species. These models will leverage data collected from various sources, such as
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, fairness). Provenance and integrity of machine learning pipelines. Generative content authenticity. Cyber-physical machine learning systems. Scalability of properties from small to large models. In
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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qualities required for such purposes. Digital terrain models will be used to efficiently map cultural remains under forest canopies. The second project (Mapping Natural Forests in Norway) focuses on mapping
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contribute to the design and development of Machine Vision approaches for the quantitative analysis and phenotyping of agricultural systems: Training/Development of computational models for the quantitative
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will contribute to projects focused on developing advanced machine learning models to quantify phenotypic traits of crops, including corn, soybean, and other selected species. These models will leverage