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publications in high-impact venues. Experience with machine learning frameworks (e.g., PyTorch, JAX) and / or computational materials methods is essential. Additionally, the candidate should possess an excellent
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language requirement of the UK HEI; Have a background or a proven interest in AI foundations and its application in civil and environmental engineering, including machine learning, sustainable construction, climate
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programming language Experience with statistical inference or machine learning methods (e.g. ABC, Bayesian modelling) A proven publication record with at least one first author publication in a peer-reviewed
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domain in the design of deep learning algorithms for cardiovascular disease detection. 4. REQUIRED PROFILE: Admission requirements: Master's degree in Biomedical Engineering, Computer Engineering
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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staff in exhibit preparation, including object research, selection, mounting, and installation as needed. Fellowship Learning Goals Learn and follow recognized standards and policies for collection
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into operational use cases. Prepare data collection frameworks and work on fish health monitoring datasets for machine learning training and benchmarking. Support the development of translational “lab-on-farm
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Responsibilities: To perform pioneer research in scent digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different
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People from Deepfakes Project. We are looking for a software/machine learning engineer (or similar) to work in an interdisciplinary team reporting to Dr Sophie Nightingale (Principal Investigator
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experience of conducting quantitative research, with strong writing skills. About us The Faculty of Health at the University of Plymouth delivers exceptional clinical and academic learning, social engagement