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, or computer science A PhD or equivalent professional qualification and/or experience in the field of machine learning for biology and mathematical modelling Strong planning and organising skills Excellent written and
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purposes. We are looking for a confident, organised researcher who can evidence: A PhD, or equivalent in statistics, machine learning, data sciences, or closely related discipline. OR near to completion of a
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purposes. We are looking for a confident, organised researcher who can evidence: A PhD, or equivalent in statistics, machine learning, data sciences, or closely related discipline. OR near to completion of a
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Applicants are invited for the posts of Postdoctoral Research Associate or Research Fellow in Machine Learning to work with AI Researchers in the Centre for AI Fundamentals at the University
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tailored to your career goals. You will have, or be close to obtaining, a PhD in deep learning, computational geosciences, computer sciences, mathematics, or physics, and have experience of developing and
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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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, output validation and reporting. Developing integrative strategies for a diverse set of data, integrating the outcomes to inform future projected trend analysis. Applying statistical and machine learning
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background in AI/NLP or speech technologies, with experience in designing and implementing machine learning models. Proficient in software development, including Python, model integration, and system
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, integrating the outcomes to inform future projected trend analysis. Applying statistical and machine learning to project future data analysis. Managing and analysing large data sets using efficient data
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demand. Responsibilities Apply machine learning techniques, statistical modelling, and chemometric methods to extract meaningful biological insights from multivariate data and complex GCxGC-TOFMS datasets