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, including uncertainty estimation, risk-aware prediction, and data-efficient learning - Continual, transfer, and meta-learning, with emphasis on sim-to-real and real-to-sim generalization - Applied machine
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to take part in the supervision of PhD students and the teaching activities of the research group. Your network and team Currently, our research group consists of 1 professor, 1 lecturer, 1 staff scientist
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intelligence, data analytics, uncertainty analysis, probabilistic modelling, or statistical learning you have experience working on relevant research or project activities involving machine learning or data
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model organisms in their work and are pushing into emerging model and non-model organisms that are proving uniquely valuable in particular studies. To learn more about our department, https
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on real-world learning, community and economic impact, and the global perspective of its faculty, students and alumni. For more information about the UB School of Management, visit http://mgt.buffalo.edu
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measurement technique development, atmospheric modelling, and advanced methods for integrating observational and model data through data assimilation and machine learning. About the research project The overall
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information on benefits and eligibility, please visit: http://uhr.rutgers.edu/benefits/benefits-overview . Posting Summary The Rutgers School of Nursing invites applications for non-tenure track faculty
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Sessional Lecturer - PPG2012H-S-Topics: Applied AI Systems & Governance: Technology, Policy & Practi
-world policy applications to equip students with the knowledge and tools needed to engage with AI at both strategic and operational levels. Students will learn how modern machine learning models work
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collaborative and international projects Experience/knowledge in HIL systems Hands-on lab experience and/or interest Knowledge on Machine Learning, or other AI techniques Team Worker Initiative in Research and
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) equipped with a cryogenic stage for surface analysis - Develop computer code (e.g. Python) for the development and analysis of optical cavities Qualifications § PhD degree in Materials, Electrical