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to advanced control design and system optimization. Our specialty is developing embedded control, estimation, and identification algorithms that directly interface with physical hardware. We work closely with
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immunocytochemistry, electrophysiology & live cells imaging. The aim of the projects is to approximate cellular phenotypes in an in vitro model of Bipolar Disorder (BD), & attributing abnormal network activity
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full-time, fixed-term position for a duration of two years. The role is based within the Department of Computer and Information Sciences at Northumbria University, located in Newcastle upon Tyne. About
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computing. You will join the Vision & Human-Robot Interaction (VHR) Group, which brings together researchers working at the intersection of computer vision, robotics, and assistive technologies. The team is
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computing. You will join the Vision & Human-Robot Interaction (VHR) Group, which brings together researchers working at the intersection of computer vision, robotics, and assistive technologies. The team is
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future tasks: Active participation in research, teaching & administration, which means: You plan and conduct your own research projects in the field of computer-assisted drug design. You publish your
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, computer-aided decision support systems Previous experience with using deep learning models (e.g., convolutional neural networks, autoencoders, transformers) for academic research Documented experience in
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, computer-aided decision support systems Previous experience with using deep learning models (e.g., convolutional neural networks, autoencoders, transformers) for academic research Documented experience in
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and facilities, including Durham's High-Performance Computer, Hamilton (15,616 CPU cores, 36TB RAM and 1.9PB disk space). As part of the Northern 8 cluster, members of Durham University can also submit
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immunocytochemistry, electrophysiology and live cells imaging. The aim of the projects is to approximate cellular phenotypes in an in vitro model of Bipolar Disorder (BD), and attributing abnormal network activity