19 big-data-and-machine-learning-phd Postdoctoral positions at University of Liverpool
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To undertake research on the evolution of prokaryotic pangenomes using machine learning and AI approaches. The work will involve the analysis of large prokaryotic genome datasets, the development
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, artificial intelligence/machine learning, digital twins, and blockchain technology for operations and maintenance. This position is part of the Maritime Future Fuels Training Plan project, which aims
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to conduct multidisciplinary research around robot learning for autonomous robotic chemists, with a background of excellent research outputs across Robotics and Machine Learning, ideally with a background in
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the growth of mastitis-causing pathogens. Performing on-farm environmental sampling of dairy cubicles and teat-end swabbing to gather real-world data. Evaluating the relationship between cubicle hygiene
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epidemiology, data science, and policy to produce high-quality, policy-relevant evidence with real-world impact. You should have a PhD (or near completion) in public health, epidemiology, data science, applied
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prison sites, recruitment and data collection, and complex data analysis. You should have a PhD (or near to completion) and an Undergraduate degree in psychology or a relevant discipline. You will have
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fingerprint identification (RFFI) for Wi-Fi. You will design novel RFFI algorithms and further evaluate their performance using practical testbeds such as software-defined radio platforms. You should have a PhD
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to meetings with industrial collaborators if required ¿ Engage with your own personal development opportunities. You should have a degree in Chemistry and a PhD in polymer chemistry. All applicants must
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. An ability to use diverse methods in the pursuit of applied knowledge is preferred and a PhD, completed or near to completion, in a relevant discipline required. You will have experience of working
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should have a PhD degree in Biochemistry, Biomedical Science, Biological Sciences, Omics science, Microbiology or a related discipline and experience of applying mass spectrometry in human signalling