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: 12 September 2025 Apply now Are you a data scientist interested in designing and implementing process-informed machine learning and uncertainties quantification methods? Join us as a postdoc and work
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learning. The post-holder will be familiar with the use of these techniques and experience of dataset construction and data mining will be essential. The successful applicant will have completed an MPhil/PhD
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that the candidate has prior research experience in one or more of the following research topics: Free space optical communication Visible light communication DSP for coherent optical communication Machine learning
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this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models
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. Experience in machine learning methods is also required. Additionally, it is critical that the individual has experience in working with epilepsy patients, data collection via the NeuraLynx iEEG system, data
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: PhD or equivalent degree in Robotics, Computer Science, Machine Learning, AI, Control Engineering, or a related field. Excellent programming skills and experience with related tools and software. A
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skills and experience: Essential criteria PhD qualified in relevant subject area* Experience developing deep learning segmentation models Experience with Pytorch, MONAI, CUDA or equivalent software
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degree (PhD or equivalent) in computer science, data science, statistics, bioinformatics, or a related discipline A strong publication record in machine learning, computer science, bioinformatics
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Education and/or Work Experience M.D. or PhD Required Qualifications Ph.D. in any of the following areas: Neuroscience, Pharmacology, Physiology, or Biochemistry. Certificates/Credentials/Licenses Ph.D. in
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Postdoctoral Research Associate in Forest Resilience, Climate Change, and Human Health in the Amazon
illnesses. The post holder will also co-supervise a PhD student who will be involved in the same project. This is a highly interdisciplinary project combining forest ecology, remote sensing, machine learning