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Field
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programme on the cybersecurity of connected medical devices and systems. The research programme represents a collaboration between the United Kingdom and Singapore and will be based at the newly established
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the REVEAL project, in which vagus nerve implant patients will be stimulated with various settings, and corresponding responses will be collected across body systems. Multiple types of computational and
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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Deep Digital Phenotyping (DDP
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pipeline. Optimize the photometry algorithm in crowded galactic fields. Generate any new simulated data required by the RISE pipeline development. We work a regular work week. We encourage continuing career
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description The BM2 Lab is an interdisciplinary research group at the interface of physics, computer science & biology interested in understanding morphogenesis, bio-mechanics and their interaction in living
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quantum information, develop techniques for quantum control and measurement, build quantum computing hardware and software, and explore novel applications. Our main interest is to propose quantum algorithms
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and/or scientific computation, scientific software and algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology
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algorithmic fairness Formulation of new problems and research directions and translating topical issues into algorithmic problems Designing new algorithms and investigating their performance on synthetic and
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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Performance . About You The successful candidate will play a key role in the development and validation of computational tools that integrate spatial transcriptomics, algorithmic methods, and machine learning