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Carlo simulation, deterministic methods, and AI/ML-assisted techniques. The study seeks to improve predictive accuracy for key performance and safety parameters while addressing the challenges associated
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porous media. Proficiency in scientific programming (Python, C++, or Fortran) is essential. Demonstrated strong analytical and problem-solving skills, with familiarity in numerical methods and an interest
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, nonlinear dynamical systems, robotics, and formal methods to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will
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before the deadline. Computational haemodynamic modelling provides a powerful framework for linking blood flow dynamics with cardiovascular disease, using in silico approaches to systematically study flow
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discipline. Programming skills in any language and knowledge of numerical methods for solving differential equations are highly desirable. Knowledge of heat transfer is advantageous. Applicants are expected
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++) and confidence working with numerical tools. Desirable: Familiarity with heat transfer/thermodynamics and/or finite‑element methods. Prior exposure to reactor physics (deterministic or Monte Carlo
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: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing • Ability to work across disciplines and collaborate with academic and industrial teams Desirable: • Experience in
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particular, we will use topology and shape optimisation methods to compute the optimal domain shapes that can stabilise solutions with desired/prescribed properties. We will use methods from inverse problems
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that you apply early as the advert may be removed before the deadline. This PhD project aims to develop a virtual tabletting laboratory by creating computational models that capture the multiscale mechanics
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crucial but challenging task due to the lack of standardised measurement techniques. The research will explore and refine "indirect" or "bulk" calibration methods, using characterisation machines to match