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to science. This is the first large-scale study of its kind, and your results will establish a legacy of scientists working with funding councils to defend their research. Cutting-edge machine learning
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scales, lubrication and wetting, biophysics as well as machine learning or metamaterials. For experimentalists, we particularly interested in applicants aligned with our existing research infrastructure
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our growing team of Associate Lecturers Online to work on our MSc Psychology, MA Education, MBA and MSc Business and MSc Computer Sciences online suites. This fully remote role offers the opportunity
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be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD awarded in Electrical or Computer Engineering Knowledge about Deep Learning
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which there exists extensive experience in the areas of machine learning, biostatistics, and medicine: Dr Yanda Meng and Dr Tianjin Huang (Machine Learning), Prof Yalin Zheng (AI in Healthcare), A/Prof
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Applied AI for Engineering, Robotics and Automation, Computer Engineering, and Electrical and Electronics Engineering. The successful candidate will contribute across the School of Engineering but will play
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Assistant or Research Associate levels, to contribute to cutting-edge research in machine learning for time series health and care data with opportunities for innovation, interdisciplinary collaboration, and
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Liverpool where, in the School of Computer Science and Informatics, we have an active group of PhD students, postdocs, and academics working at the intersection of Machine Learning, Verification and
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, and quantum machine learning for NISQ architecture(s). This post is advertised as full-time. We are open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of
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methods suitable for legged systems in physically-realistic simulated environments and on real robots. You should hold or be close to completion of a PhD/DPhil in robotics, computer science, machine