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We are looking for an adventurous researcher to explore Monte Carlo methods for radiation transport (as part of the EPSRC grant, MaThRad). The Cambridge Nuclear Energy group is developing a modern
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Sequential Monte Carlo Methods for Bayesian Inference in Complex Systems Department of Automatic Control and Systems Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova Application
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with Monte Carlo modelling skills and a good understanding of the basic radiation dosimetry principles. Optional experimental work spans molecular biology, in vitro cell studies and preclinical in vivo
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, with a relevant PhD is desirable: Interacting particle systems for Monte Carlo methods and rare event simulation Statistical physics for transport modelling Branching structures and or stochastic
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in high-level electronic structure theory such as coupled cluster or diffusion Monte Carlo is highly desirable, as is a track-record of successful collaborative work. We particularly welcome applicants
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or diffusion Monte Carlo is highly desirable, as is a track-record of successful collaborative work. We particularly welcome applicants from women and / or candidates from a BME background for these vacancies as
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well as impactful combination therapies. The work spans cellular and preclinical tumour and health organ dosimetry and can be easily carried out by somebody with Monte Carlo modelling skills and a good understanding
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multiple expert elicitation. PINCODE aims to address the challenges in such pooling inference, with implementing either exact or asymptotically exact Monte Carlo algorithms. The project is a collaboration
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, including sequential Monte Carlo methods, Gaussian processes and Bayesian compressed sensing. Applicants from different backgrounds are encouraged to apply depending on the specific nature of the project
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with LAMMPS and/or OpenMM, (4) have experience in developing or applying coarse-grained models, polymer models, and/or in developing and applying Molecular Dynamics and/or Monte Carlo simulations