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physical laws, or an implicit form of extra data examples collected from physical simulations or their ML surrogates. In medical domains, patient data is typically distributed across multiple hospitals
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’, i.e. policies that deal with problems after they occur, rather than long-term prevention. By developing innovative simulation models that incorporate the life-course consequences of policy options, your
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the molten pool. However, these models are computationally intensive and impractical for widespread simulations of large-scale part deposition. This project aims to develop a novel FEA-based approach
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PhD Position: Activating Heritage as a Mediator for Dialogue and Belonging in an Era of Polarization
best represents your work Proof of English proficiency – more information in this link A copy of your diplomas with a list of course grades Reference letters from two academic referees Please send in
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the Principal Investigator and CSGS Director - Mark Pennington This is a full-time post (35 hours per week), and you will be offered a fixed-term contract from 1st September 2025 to 31st May 2028. About
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climate states. Key responsibilities To carry out and complete your own research towards a doctoral degree. Design, run, and analyse equilibrium simulations with a coupled Earth-system model of intermediate
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schematics, layout, test benches and simulation Design blocks as Op-amp, LDO, VCO, PLL, voltage or current reference circuits Develop innovative analog design solutions to enhance signal integrity, noise
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Applied mathematics, fluid mechanics, high-performance computer simulations. Full time, fixed term position (3 years) at Hawthorn campus $34,700 per annum (2025 rate) About the Scholarship Higher
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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Simulations (LES). The analysis will be performed together with teams at the Helmholtz-Zentrum Hereon that focus on ocean turbulence and machine learning as a part of the TRR181 Collaborative Research Centre