665 postdoc-in-thermal-network-of-the-physical-building Fellowship research jobs in United Kingdom
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. The post is offered at 80%FTE on a fixed term basis for 3 years, beginning 1 January 2026. ABOUT THE TEAM The Social participation for Universal health Coverage: evidence-informed NetWorks Advancing
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Athena Swan Gold Award . To help you succeed, we published Candidate Guidance to provide support on the application and interview process. Discover our benefits, visit Your Benefits website. We welcome
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the application and interview process. Discover our benefits, visit Your Benefits website. We welcome applications from UK, Europe and worldwide and aim to make your move to the UK as smooth as possible. Visit the
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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networks bridging metadata, text mining and FAIR data principles Contribute to publications, funding bids, and supervision of students You will have: A PhD or equivalent experience in life science or
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to make applications for funding of a higher degree (PhD/ DM) and doctoral fees which are not included in this post. PhD/DM training for clinicians in the School of Medicine is closely supervised and
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technical knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques
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Develop skills and networks to become a successful independent researcher, including through grant writing, independent papers, consulting, trainings The skills, qualifications and experience required
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networks bridging metadata, text mining and FAIR data principles Contribute to publications, funding bids, and supervision of students You will have: A PhD or equivalent experience in life science or
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-life environments. The Role: As Research Fellow on the COG-MHEAR project, you will have the opportunity to use your strong background in deep neural networks and multimodal hearing-aid signal processing