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Field
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Department BSD MED - Genetic Medicine - Chowdhury Research Staff About the Department The Section of Genetic Medicine continues to shape the future of personalized medicine with successful research
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. Responsibilities: Conduct research on automated reasoning and proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and optimization. Participate in collaborative research projects
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interpretable framework for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured
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neuroimmunology program in the Department of Neurology at Yale School of Medicine, with close ties to genetics, computational biology, and clinical trial groups, including partnerships with pharmaceutical
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sitting for long periods of time Shift 20 hours per week / hours vary Job Summary This position will support for an artificial intelligence project. Responsibilities include developing algorithms and
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oncology patients will see multiple providers on their first visit, need diagnostics and additional visits such as genetics. This is identified prior to the patient coming for the first time. Thiswork must
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exploratory data analysis from complex and high-dimensional datasets. Uses a flexible, analytical approach to design, develop, and evaluate predictive models and algorithms that lead to optimal value extraction
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implement the computational algorithms, automate data analyses workflow, curate published pan-cancer datasets, and build prediction models for novel transcriptome features analyses identified using bulk and
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-free algorithms for real-time optimization of turbine operating conditions (e.g., yaw set points). Other projects may be assigned by the supervisor depending on skills and technical needs. The successful
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results Job Description Primary Duties & Responsibilities: Designs, develops, and implements: Algorithms and computer software for omics-based data sets [high-throughput, massively parallel genomic