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Field
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formats available in conventional hardware are often too accurate for the needs of machine learning: they do not improve the quality of the trained model but may deteriorate it by causing overfitting
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situated in the College of Creative Arts, Technology and Engineering. The purpose of the role is to support teaching and learning through the delivery of modules in conjunction with the Course Leader
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, you will work to develop and implement machine learning approaches to identify lakes and lake drainage behaviour, and to deploy these workflows at scale. Based at Lancaster University, you will join our
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training. Teaching and Learning is important to the Department and University and all applicants will be expected to be able to play a full role in teaching across the undergraduate and postgraduate
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substantial experience in developing complex AI or machine learning models, particularly using Python and associated libraries such as PyTorch, TensorFlow and Scikit-learn. Demonstrated expertise in version
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, Nutritional Sciences and Women's Health cluster) for REF was rated as world-leading or internationally excellent. We use this expertise to teach the next generation of health professionals and research
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/or machine learning methods, and an interest in applying these tools to urban and housing policy questions. The Fellow should demonstrate potential for producing high-quality research and a strong
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spectrometry techniques (QTOF, MALDI-TOF-TOF and triple quadrupole). Our ideal candidate will have: Experience in water, mass spectrometry and data engineer A PhD (or equivalent) in mass spectrometry related
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qualification/experience in a related field of study. The successful applicant will have expertise in statistical modelling, epidemiology or machine learning and possess sufficient specialist knowledge in
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, ideally at postgraduate or professional levels. A solid grounding in applied data science, including statistical methods, machine learning, and Programming. Experience delivering flexible, inclusive