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generation sequencing and microbiology. The successful candidate will work closely with an interdisciplinary team of academics at University of Nottingham. The role will include data analysis via machine
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machine learning, bioinformatics, sequencing and microbiology. This role is available on a fixed term basis until 31st May 2027. Hours of work are full time (36.25 hours). Job share arrangements may be
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takes input from a machine-learning platform (Python-based) to produce new compositions. A cobot delivers the material to the furnace or a plasma spray to produce new coatings. The proposed research is
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telemetry equipment. They will be responsible for ensuring high quality twins, and will interact with the project team on the development of effective machine-learning models to be deployed within these twin
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analytical skills and substantial experience in machine learning at scale are required. Detailed information on the requirements for the role can be found in the further particulars. The position is for 1 year
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the power of machine learning, you will explore the interplay between metabolomic and proteomic data and depression to transform our understanding of this pervasive disorder. Based at King’s College London, a
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collaborators and travel to academic conferences and project meetings to present the work. Successful candidates must hold (or close to completing) a PhD in a relevant subject. Knowledge and experience in
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research assistant in applied adversarial machine learning. This exciting opportunity will contribute to the D-XPERT (AI-Based Recommender System for Smart Energy Saving) project. The D-XPERT project
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candidates must hold (or close to completing) a PhD in a relevant subject. Knowledge and experience in computer vision is required. Experience of efficient ML techniques, edge AI hardware platforms, low-power
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Applications are invited for a Research Assistant or Research Associate to work on efficient machine-learning systems for earth observation. The post holder will be part of the Computer Architecture