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opportunities exist at the intersection of mathematics, computer science, statistics, and their scientific applications, with the lines between theory, algorithm development and software implementation often
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the Division of Population Sciences at Dana-Farber our work involves diverse scientists from various scientific domains and collaborations with division faculty members who are experts in human genetics
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 8 hours ago
, reinforcement learning, and computational game theory to address this gap. Fellows will contribute to advancing the next generation of models, algorithms, and system architectures for autonomous systems, multi
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POSTDOCTORAL COMPUTATIONAL BIOLOGIST FELLOW, under the Collins Genomics Lab to lead pioneering studies of genetic risk for cancer based on large-scale genome sequencing datasets towards the ultimate goal
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. This is primarily for an NIH-funded project developing multimodal variational autoencoder models and probabilistic trajectory analyses for latent spaces formed from neural, genetic, and behavioral data
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activities will be developed in the Power Electronics area, focusing on programming control algorithms on a Xilinx FPGA. This project aims to implement internal fault tolerance in a power electronics
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architectures for explainable dual-process computation Design and development of deep neural network architectures and algorithms for the implementation of dual process computation approaches that improve
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analyses for latent spaces formed from neural, genetic, and behavioral data. Mission Statement Michigan Medicine improves the health of patients, populations and communities through excellence in education
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with applications to aerospace systems Designing, implementing, and testing control algorithms in simulation and hardware platforms Contributing to publications and reports; presenting research findings
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees