174 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" positions at University College Cork in Ireland
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in children. Post Duration: 24 Months, Part-time (0.5FTE) Salary: €46,905, per annum, Personal Rate, pro rata (0.5FTE) For an information package including further details of the post see https
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process see https://ore.ucc.ie/. The University, at its discretion, may undertake to make an additional appointment(s) from this competition following the conclusion of the process. Informal enquiries can
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process see https://ore.ucc.ie/. The University, at its discretion, may undertake to make an additional appointment(s) from this competition following the conclusion of the process. Informal enquiries can
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and/or an international police clearance check may form part of the selection process. For an information package including full details of the post, selection criteria and application process see https
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fluids, quantum spin liquids and exciton fluids. • Data Management, Analysis & Machine Learning Knowledge and experience in high volume image-array data acquisition and management, development of custom
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the selection process. Project: ELEVATE For an information package including full details of the post, selection criteria and application process see https://ore.ucc.ie/ The University, at its discretion, may
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clearance check may form part of the selection process. For an information package including full details of the post, selection criteria and application process see https://ore.ucc.ie/. The University
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candidate will support the delivery of the 2-year MSc in Diagnostic Radiography and MSc Radiation Therapy programmes and teach and coordinate elements of the postgraduate MSc in Medical Imaging and Radiation
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across a wide range of research projects. This role requires excellent communication and interpersonal skills, computer literacy, and data management. He/she is a key member of the project team and should
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) approaches. Design predictive maintenance algorithms using machine learning, statistical learning, and digital twin-based models to anticipate failures and optimise maintenance interventions. Integrate AI