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communication journals Demonstrable proficiency in advanced quantitative data analysis: applied machine learning, statistical analysis, and handling complex data. Programming skills in Python and R are essential
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the University's research culture and collaborative profile. Qualifications: PhD in Computer Science/AI or a closely related field. Extensive research experience in machine learning, deep learning, and self
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for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. The post-holder will also be responsible for writing up the findings
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to assess potato dormancy break, including: data collection, processing, AI model development and classification accuracy assessment. Involved in supporting an electrophysiology-based machine learning model
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indicators Experience in data visualisation and communication of research findings Track record of working effectively in international, multi-disciplinary teams Desirable Skills: Experience with machine
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classification accuracy assessment. ii) Involved in supporting an electrophysiology-based machine learning model to predict dormancy break. You will be part of a multidisciplinary academic and industry team
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development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning algorithms might support the wider integration of, and uptake
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machine learning. Supervision will be provided by Prof. Ali Mazaheri, as well as Prof. Fang Gao Smith, and Prof. Helen McGettrick. The successful candidate will have a strong background in psychology
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processing, machine learning, neurophysiology, and human-centred design. You will be central to the execution and data collection of a major prospective research study in hospitalised patients. As a Clinical
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. The aim of project is the safe integration of machine learning methods within the biopharmaceutical development process. This project offers an opportunity to be at the forefront of interdisciplinary