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interactions. We’re Looking for Someone With: Strong expertise in protein structure prediction, molecular modelling, and docking. Proficiency in LINUX, bash scripting, and high-performance computing environments
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, computational biology, physics or a related discipline, and have experience of agent-based and/or continuum modelling, analysis and simulation. You will have experience of programming in Python and C
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, spatial, and social data into digital twin models for scenario testing and policy simulation. Adapt co-design methods to local contexts in demonstrator sites (Portugal, Sweden, Italy). Contribute to policy
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humanised mouse models with the same mutations as patients, all of which are currently available in the laboratory. Minimum qualifications are PhD and/or MD with expertise in molecular and cellular biology
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split 0.5FTE on the UKRI Medical Research Council funded project STARS: Sharing Tools and Artefacts for Reproducible Simulations in healthcare ; and 0.5FTE on the Health Service Modelling Associates (HSMA
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of agent-based and/or continuum modelling, analysis and simulation. You will have experience of programming in Python and C++, or demonstrated ability to rapidly acquire fluent knowledge of new programming
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Knowledge and prior experience of model simulation, recovery, and fitting of decision-making data Excellent verbal and written communication skills Ability to work independently as well as within a team. Good
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learning “emulators” of multiple ice sheet and glacier models, based on large ensembles of simulations extending to 2300. The simulations will be from two international projects aiming to inform
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navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
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the proto-oncogene c-Myc biology of the tumour suppressor Arf and p53 Reversibly switchable mouse models and technologies Downloading a copy of our Job Description Full details of the role and the skills