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students and PhD students. Applicants will have, or be close to completing a PhD in a relevant field and possess relevant experience, in the area of probability or statistical machine learning. They will
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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 is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role. This post would be ideal for an ambitious and
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have a strong knowledge of core computing topics. We are specifically seeking expertise in Machine learning Data management Artificial intelligence Data visualisation and analytics Data mining Cloud
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, including linear-scaling density functional theory (LS-DFT), quantum embedding, and machine learning potentials, to systematically characterise unidentified colour centres, and explore this vast
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machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role. This post would be ideal for an ambitious and
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computer science or mechanical engineering. The candidate will have programming experience, particularly on the development of machine learning pipelines. The University actively supports equality, diversity and
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join the Physical Mathematics Laboratory, a team of researchers led by Dr. Stuart Thomson with interests in physical applied mathematics, machine learning, and robotics. The successful candidate will
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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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, Bayesian Statistics with a focus on nonparametric methods, Bayesian Computational Methods, Extreme Value Theory, Biostatistics, Probabilistic machine learning, Medical sciences and engineering applications