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Field
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institutes. Teaching Responsibilities In the role of a lecturer, the successful candidate will teach undergraduate courses in Electrical & Computer Engineering (ECE) program. The expected workload for the in
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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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machine learning and artificial intelligence. The successful candidate will support teaching on a new postgraduate MSc in Data Science with Machine Learning and AI, as well as more general teaching
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influence clinical practice. We welcome applications from candidates with following backgrounds: Candidates with strong experience in medical image analysis, machine learning (especially deep learning) and
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postgraduate programmes to military and civil service students, responding to the ever-evolving needs of military education. Our teaching equips professionals with the knowledge, creativity and intellectual
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a range of methods including causal inference, signal processing and machine learning to investigate whether and how acceleration data from flying birds can be used to characterise changing levels
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stakeholders Knowledge of new and emerging digital tools and their application within urban planning and design (for example, the growing use of AI/Machine Learning in local planning authorities) Lecturer Grade
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, Bayesian Statistics with a focus on nonparametric methods, Bayesian Computational Methods, Extreme Value Theory, Biostatistics, Probabilistic machine learning, Medical sciences and engineering applications
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machine learning and artificial intelligence. The successful candidate will support teaching on a new postgraduate MSc in Data Science with Machine Learning and AI, as well as more general teaching
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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