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Field
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, algorithm development, AI, and machine learning, supporting fusion energy engineering through advanced simulation and workflow optimisation. You will work on impactful, collaborative projects across academia
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AI Research Specialist Research Track COLLEGE OF MVLS GRADE 7 Background Job Purpose To develop, test, and implement AI/Machine Learning models for the analysis of real-world healthcare applications
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who is also skilled in bioinformatics, image analysis, and machine learning. You’ll be part of a dynamic, supportive, and forward-thinking research environment committed to making real clinical impact
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, Mathematical & Engineering Sciences. The post holder is required to hold a PhD degree in Computer Science or other relevant discipline. They will have skills in natural language processing and deep learning
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. Ability to collaborate with colleagues and stakeholders will be essential. We particularly welcome candidates with experience in psychoacoustic listening tests and machine/deep learning. A PhD in a relevant
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. Molecular simulation methods and quantum theoretical calculations in principle can address this but have hitherto struggled with tackling such challenging systems. With the emergence of machine learning
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machine learning and deep learning frameworks, development of API’s and dashboards alongside proficiency in programming skills, will stand you out from your peers. For all our roles we are searching
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candidate should have the following: Relevant postgraduate/PhD qualification Experience of learning, teaching and assessment within Higher Education Appropriate evidence of professional practice and external
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experience of treatment. The overarching aim of the project is to use machine learning methods to understand why many people who are referred for treatment will drop out prematurely. To do this, two studies
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data gaps by combining process simulation (e.g., Aspen software) with machine learning techniques. By developing accurate, large-scale life cycle inventory data using enhanced digital tools like deep