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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored
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, machine learning or causal inference for estimating, understanding and forecasting demographic and health outcomes, at the individual and aggregate levels, including as they relate to life course and socio
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Required qualifications: PhD in evolutionary ecology or related fields Strong track record of publishing scientific research Familiarity with urban evolutionary biology and wildlife biology. Strong ability
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materials for new grant applications, and manage laboratory equipment. Two years minimum of postdoctoral training or equivalent is required. Qualifications: PhD in Physics, Engineering of other closely
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reinforcement learning and machine vision. Experience with ROS and the ROS ecosystem Special Requirements: Applicants cannot have received their PhD more than five years prior to the date of application and must
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upon future funding. Qualifications Required Education and Experience Appropriate PhD in a related field. Preferred Qualifications Experience with machine learning and deep neural network techniques
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. Demonstrated expertise in interdisciplinary research with technical and clinical teams and patients desired. Experience leveraging machine learning for human musculoskeletal biomechanics applications desired
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of state voting legislation for the Voting Laws Roundup. This work includes developing computational tools (e.g., using large language models, machine learning for text analysis and classification, etc
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Post Doctoral Researcher in Digital Twins CO2-to-Protein production in collaboration between the ...
collaboration between the Department of Electrical and Computer Engineering and the Novo Nordisk Foundation CO2 research center, Aarhus University, we aim to address this opportunity by developing digital twins
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the HNSCC team, including Taran Gujral (machine learning-enabled drug screening), Slobodan Beronja (mouse models of HNSCC), and Patrick Paddison (functional genomics). This work will encompass a broad array