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                level $108,156 p.a.) plus 17% super Pioneer research in modelling heterogeneous populations through collaboration with leading institutions to advance mathematical biology. Apply now for this exceptional 
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                Award at the University of Sheffield. We aim to develop retrieval-augmented, multi-modal, and explainable Large Language Models (LLMs) for healthcare fact-checking. Reliable healthcare information is 
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                $85,555 - $116,094 p.a. (*PhD entry level $108,156 p.a.) plus 17% super Pioneer research in modelling heterogeneous populations through collaboration with leading institutions to advance mathematical 
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                Vacancies PhD Opening: Reinforcement learning in human neuromusculoskeletal models for the control of human-inspired musculoskeletal robots. Key takeaways We’re seeking for motivated candidates 
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                or nats, balances model complexity against goodness-of-fit. It is essentially a formal implementation of Occam's razor. A key advantage of MML is that the message length provides a universal gauge for 
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                on formal verification and reachability analysis using logical zonotopes, with applications in digital circuit verification and model checking. Candidates should have a strong mathematical background and 
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                : 278915095 Position: Postdoctoral Associate: Integrating political economy insights into energy modeling Description: The Peng group in the School of Public and International Affairs and the Andlinger Center 
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                seeking for motivated candidates that are interested in developing computer models of the composite human neuro-muscular system that combine detailed musculoskeletal geometries, muscle-tendon models and 
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                methods to ultimately let dermatologists continually update multi-modal machine learning models. Our research objectives are to 1) develop novel model editing methods for multi-modal models, with a focus on 
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                models for microelectronics materials Curate, manage, and integrate heterogeneous datasets from experiments and simulations Collaborate closely with experimental teams to benchmark and refine computational