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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
nano-structures. In this project, we will combine numerical models, experiments, and artificial intelligence (AI) to guide the design of specific DNA nanoconstructs. The primary goal is to build an AI
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numerical flow modeling, with a focus on transient regimes, species transport, and the application of RANS and LES turbulence models, primarily using ANSYS CFX and Fluent; Skills in conducting experimental
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on regional-scale modeling and simulation of subsurface flow and geomechanics, including associated numerical and computational methods. Topics may include upscaling, numerical analysis, and the design and
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. Key skills Proven experience in mathematical modelling, particularly in the context of NCD's Highly numerate, understanding of stochastic calculus Proficient in programming languages, including R
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Energy Modelling and Simulation. The Dworkin group focuses primarily on numerical modelling, but also performs some experiments as needed for certain projects. The group currently has 2 Postdoctoral
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engineering, computer science applied to urban infrastructure, data science, or a related field. Expertise in numerical modeling and simulation for urban systems. Proficiency in digital twin tools and platforms
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with data-based models and numerical dynamo simulations. The successful candidate will contribute to studies of time-averaged geomagnetic field morphologies and core dynamics during transitional events
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Qualifications PhD in the field of Planetary Science, Astronomy, Geoscience, or a related discipline by the start date of the position. Experience with numerical modeling and programming. Experience with modelling
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 1 day ago
must be able to use a combination of numerical techniques and analytic perturbation theory to model more accurately the ringdown phase of black hole mergers, determining the non-linear contributions
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understanding of geomagnetic field evolution across different timescales, including both stable and extreme periods. This will involve working with data-based models and numerical dynamo simulations