50 machine-learning-"https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" Fellowship positions at University of Michigan
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systems using computer vision, quantitative image analysis, deep learning methods for detection, diagnosis, and quantitative analysis of abnormalities with multimodal data, including clinical and
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Apply Now Job Summary An exciting opportunity has been created for an experienced researcher to join the Alumkal Laboratory (https://alumkal.lab.medicine.umich.edu/ ) at the U of M Division of Heme
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molecular analyses to animal models to human applications. More information about the Kaczorowski lab can be found at http://kaczorowski.lab.medicine.umich.edu Mission Statement Michigan Medicine improves
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development of transparent, closed-loop control system for individualized diuretic closing including the validation and advancement of machine-learning and control algorithms, building production-oriented
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, spatial transcriptomics, single cell RNAseq, and multi-omics data integration. Lead graph-based network and machine learning analyses of tumor immune microenvironment architecture. Collaborate with wet lab
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research projects in computer vision, machine learning, AI, and robotics. Projects may include physically-grounded AI guidance agents, modeling of multimodal data, and generative AI systems for situated
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automation, computational drug design, machine learning, and software engineering. The ideal candidate will contribute to innovative research and the development of advanced computational tools within our lab
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behaving mice, and advanced modeling + machine learning analyses. Please read more about our research at www.apostolideslab.org . Key questions we want to answer are: How do neural circuits extract
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conditions and policy outcomes. Desired areas of expertise include: dynamic and complex systems, agent-based modeling, computer programming (familiarity with R, Python, Netlogo), statistical analysis
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) in addition to Medicare and Medicaid claims. Our team also has extensive methodologic experience, including natural experiments/econometrics and various machine learning techniques. The Fellow will