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Field
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-efficient computing Developing mathematical modeling for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer
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Research Fellow in AI for Healthcare (1 FTE) – ArcHub ICUSafeNotes-School of Computer Science 011287
addressing important data-driven applications in medicine, engineering, and the physical sciences, on the basis that important applications foster novel algorithms which in turn enable new applications
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project with real clinical potential The Opportunity Are you a highly motivated and technically proficient researcher with expertise in electrochemical sensor development and biomedical engineering? We
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eHC through reduced scale testing. Lastly, you will also need to evaluate the various gas monitoring sensors performance in detecting the battery gas releases for remote monitoring and early detection
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a Research Fellow to contribute to a project focused on algorithm design in Game Theory and Fair Division. Key Responsibilities: Formulate mathematical models for research problems in computational
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Developing and integrating AI algorithms into the real development progress Preparing academic publications such as patent applications and research papers Contributing to quarterly and annual report writing
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This research project aims to establish the theoretical and algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It
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advances the mathematical foundations, algorithms, and real-world applications of epistemic uncertainty in machine learning, with a strong focus on imprecise probabilities, uncertainty representation and
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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health records (EHR), waveforms from bedside monitors, radiology images and wearable sensors. This position offers a unique opportunity to work closely with clinicians on applications of machine learning