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of this project is to add support for automatic code optimization in Tiramisu. In particular, we want to use machine learning/deep learning to achieve this. Currently, a basic automatic optimization module
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-time (1.0 FTE) Postdoctoral Fellow with a strong background in data science and machine learning. Experience with pediatric cancer and a desire to become a pediatric cancer researcher is highly desirable
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, machine learning-assisted mapping and reconstruction of brain-wide circuitry, behavioral clustering, cell-type and action-specific Cal-light tagging, closed-loop optogenetic manipulation, calcium imaging
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mentality for cutting-edge research in various fields including robotics, machine learning and systems intelligence. An exceptional opportunity to experience research in a highly inspiring international
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. using programs like PLINK, bigsnpr, regenie, BOLT-LMM, GCTA, LDSC, LDAK, LDpred1/2, PRS-CS, SBayesR, PRSice. Machine learning approaches, e.g. deep learning, autoencoders, XGboost, or penalized regression
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analyses using Danish register data and/or large genetic datasets. This may include genetic analyses, causal inference, epidemiological analyses, and clinical prediction modelling using machine learning
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 2 months ago
EXPERIENCE RESEARCHER, Open Learning, to work with the product team on identifying, defining, and refining research questions. Will conduct in-depth user research using multiple methods such as interviews
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of this project is to add support for automatic code optimization in Tiramisu. In particular, we want to use machine learning/deep learning to achieve this. Currently, a basic automatic optimization module
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this academic year. We’re looking for two types of predoctoral fellows: “Machine learning” track: you’re very skilled in machine learning, in particular large language models. Ideally, you would have a Masters in
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interested in candidates who can leverage machine, deep learning, and statistical methods to monitor species distributions and integrate biodiversity records from multimodal data sources to understand