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Field
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Responsibilities: Integrate and analyze large-scale multi-omics datasets (genomics, transcriptomics, epigenomics) to derive biological insights Apply statistical and machine learning models to identify cancer risk
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We are seeking a Research Fellow to perform research on deployment of machine-learned models for health analytics on distributed IoT/edge/cloud systems using transprecise computing and contribute
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computing, and database development and management. Background in computational modeling and machine learning. Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully
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: Bayesian Machine Learning – Led by Dr Thang Bui, this project focuses on sequential decision-making and bridging deep learning theory and practice. Applicants with expertise in probabilistic modelling
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collaboration with the world-leading Briscoe lab at the Francis Crick Institute. It is using mathematical and machine-learning methods to study the early development of the neural system and developing those
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are demonstrated knowledge related to acoustic modelling, measurement and soundscape. o Essential are demonstrated data analytic skills, ideally with machine learning or statistical modelling • Other general
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for active learning. The role will work at the intersection machine learning, high-throughput computation, and inorganic crystalline materials discovery, focusing on accelerating the design and
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key properties of quantum machine learning models—expressivity, generalization, and adversarial robustness, and the inter-play between them. These foundational insights will guide the design of novel
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leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models—expressivity
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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