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assays, to play a key role in development and deployment of cutting-edge methods to detect cancer earlier than traditional methods About You Experience of translational research using human samples
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-Watt have a long–standing track record in researching ground-breaking single photon detection and applications in quantum communications and single-photon imaging. With single-photon avalanche diode
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of using Bayesian methods in both model development and fitting. Previous experience and knowledge of research methods and study design in clinical trials. Knowledge of Good Clinical Practice (GCP) in
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(e.g. Python, R) Experience in the use of neuroimaging analysis (fMRI, MRI) to study mechanisms of brain function Previous experience of using Bayesian methods in both model development and fitting
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of using Bayesian methods in both model development and fitting. Previous experience and knowledge of research methods and study design in clinical trials. Knowledge of Good Clinical Practice (GCP) in
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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, imaging and timing applications. In this project, we will: Develop Bayesian deep learning methods for event-based data, including single-photon detections and neuromorphic camera data. Investigate Bayesian
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, or single-photon detectors (used for single-photon Lidar), that natively produce event-like data that is compatible with spiking networks. Similarly, detection events from RF (radar and electronic
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
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vision and machine learning methods for multimodal imaging and real-time analysis in colorectal cancer screening and treatment. They will contribute to the design of AI algorithms for polyp detection