144 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at University of Birmingham
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irradiated materials. This work will be under the supervision of Prof. Enrique Jimenez-Melero, UKAEA Joint Chair in Materials for Fusion (https://www.birmingham.ac.uk/staff/profiles/metallurgy/jimenez-melero
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, with significant experience in practice-based or classroom education who is capable of facilitating learning in students of all levels. You will have an in-depth understanding of the UK healthcare
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wide ranging (https://www.birmingham.ac.uk/research/medical-dental ), including a large portfolio of large-scale research programmes and infrastructure awards funded by charities, government and other
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-record in mentoring and supporting the development of early-career researchers. The profile and research interests of the group may be found at: https://www.birmingham.ac.uk/research/centres-institutes
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during
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particular emphasis on preparing analysis-ready datasets that support downstream statistical and machine learning workflows. What you would be doing You will contribute to data access workflows, database and
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across the whole range of disciplines. Activities will include exploring and facilitating new and emerging research in areas such as Big Data, AI and Machine Learning offering guidance on how copyright and
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colleagues to network and collaborate, offering opportunities to learn and develop, contributing to the delivery of the University’s objectives, and helping everyone to understand the broader context within
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. Carrying out plant room checks. Pool vacuum set up and take out, cleaning of the machine. Attending training once a month outside of normal shift pattern, lifeguard training, situational training, fire
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future of formulated polymers. We are seeking a Research Fellow in Computational Chemistry and AI/Machine Learning to advance the state of the art in understanding the degradation and biodegradation