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- Zintellect
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to work on a new study funded by the Wellcome Trust. The aim of this project is to advance understanding of the lived experiences and support needs of minoritised ethnic people with multiple long-term
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application of Hamiltonian methods to gauge theories, though you will also be encouraged to develop and pursue your own research directions. Applicants should have a PhD in physics and a track record of
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TPMS data, high-speed wheel encoders, CAN data, accelerometer data, and acoustics data. In addition to other cloud-based data for weather-friction estimates and crowdsourced vehicle data for estimating
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of Information & Computer Sciences About UMass Amherst The flagship of the Commonwealth, the University of Massachusetts Amherst is a nationally ranked public land-grant research university that seeks to expand
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Kahl (Computer Vision, Chalmers), Kathlén Kohn (Algebraic Geometry, KTH), and Mårten Björkman (Robotics, Perception and Learning, KTH). The research focuses on developing novel machine learning methods
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of their career developing state-of-the-art statistical and mathematical methods to analyze epidemic data, with the aim to increase our understanding of how pathogens spread in populations, assess the impact of
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stocks and equipment and be responsible for coordinating repairs when needed. Maintain laboratory records for experiments for publication methods and for others to repeat experiments. Correspond with other
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multiple applications in Zintellect, they must click the unique link in each email request, but will be given the opportunity to copy over what they had previously submitted. All documents must be in English
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, transportation, and more, with a special emphasis on grid resilience assessments and equity analysis. You will have the opportunity to creatively use interdisciplinary methods from computational data science
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, infrastructure regulation, trade) Interest in the geopolitics of cloud infrastructure or digital sovereignty Strong collaboration and communication skills Basic knowledge of quantitative methods (e.g. multiple