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Field
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transitions, metastability, climate system response to perturbations, and machine learning applications in climate science. You will be part of an internationally recognised research environment, where you will
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observation data, and machine learning techniques to address climate and sustainability challenges. You will contribute to the development of computational frameworks, collaborate across disciplines, and work
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subject or an Honours Degree with appropriate professional qualification and/or membership and clinical experience. A teaching qualification or acceptance of the requirement to acquire one PhD or active
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academic career and research. During the two-year appointment, SOAS Associate Lecturers will have the opportunity to gain experience in teaching and learning activities, to develop their research and/or
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Engineering, potentially including Power Electronics, Machines and Drives, Network Optimisation and Reliability, with the ability to teach our students to an exceptional standard and to fully engage in
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completion, preferably with an application of their research in a real-world setting. Coding and software engineering proficiency will be expected if relevant to their experience, e.g. for machine learning
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order to gain novel understanding of the molecular mechanisms underlying disease. About You You will have a PhD (or equivalent qualification) in computer/statistics/data science or similar field. You will
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are essential. You should have expertise in at least one of the following areas: theoretical modelling, machine learning, artificial intelligence, or numerical methods for climate systems, and be confident in
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machine learning or AI methods in healthcare research, particularly within digital trials or real-world data studies. Expertise in analysing complex digital health data, including wearable sensor data
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KTP Associate in Machine Learning ( Job Number: 25000811) Department of Computer Science Grade 7: - £39,105 - £43,878 per annum Fixed Term - Full Time Contract Duration: 30 months Contracted Hours