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Field
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of patients. The candidate will contribute broadly to the project to enable the development of new algorithms for clinical AI based on patient data from heterogeneous sources, notably language/speech-based
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science, with a particular focus on neuroscience applications. Designs AI techniques and algorithms for multimodal data fusion (e.g., MRI, EEG, cognitive and behavioral data, blood biomarkers, and
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COG-MHEAR is a world-leading cross-disciplinary research programme funded under the EPSRC Transformative Healthcare Technologies 2050 Call. The programme aims to develop truly personalized
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human disease, and developing and applying new computational algorithms to decipher it Enjoy working closely and collaboratively to solve complex biological problems Able to lucidly present complex
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Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes
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computer programming to verify the efficiency of the designed solution algorithms Analyze data acquired from the field survey Develop machine learning models for prediction and recommendation Job
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Research Fellow in Intervention Development to join the Big Data in Health Group About us Our big data in health team at the University of Southampton is based in the Primary Care Research Centre
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amplifiers Develop scripts and algorithms for analog IC design Prepare report and conduct presentation at seminars Assist the school in various teaching activities, including instructing or supervision of labs
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HiPerBreedSim project. In this role, you will leverage recent advances in working with ancestral recombination graphs (ARGs) to develop algorithms and code for simulating population genomic data, including
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. The QDA is focused on advancing quantum technologies to support decarbonisation and clean energy goals. The successful candidate will contribute to the development and implementation of precision atomic