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(large scale heterogenous data synthesis, meta-analytic studies, conceptual synthesis) Experiences and interests in shaping modern team science research and interest in super-visioning & coordinating
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hyperparameter optimization, meta-learning, and adversarial training. The general bilevel problem can be written as: min F (x, y∗(x)) where y∗(x) = arg min f (x, y), d
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generating, mobilising, and harvesting “big data” to create a dynamic and agnostic collection of information and deliver a new class of research that will enable a better understanding of the clinical
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generating, mobilising, and harvesting “big data” to create a dynamic and agnostic collection of information and deliver a new class of research that will enable a better understanding of the clinical
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Artificial Intelligence, META Center for Host-Microbe Systems Biology, the Center for Genome Function, and the Materials Science Institute. The Department’s educational mission focuses on undergraduate and
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on the strengths of the successful candidate, in either the broad direction of principle-based meta-reasoning (formal focus) or applications of automated meta-reasoning to large-scale software systems (engineering
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ideas and work together towards answering the big questions of the future. You also appreciate the exchange between disciplines, cultures and generations? We are looking for a/an University Assistant
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the analysis of large-scale health data, to systematically integrate evidence and identify patterns across diverse health outcomes. The ideal candidate will bring a proven interdisciplinary background
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, perform data cleaning. Develop machine learning algorithms, natural language processing tools, and end user tools. Develop and interpret MCMC models; develop meta-models. Analyze epidemiologic and
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techniques capable of handling: Extremely large design spaces with many interacting variables Multi-objective trade-offs (performance, power, area, sustainability, etc.) Complex constraints and architectural