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Field
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training deep learning models on biological, chemical or related datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep
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) position on the development and application of phase field methods for dynamic earthquake rupture simulations in the context of enhanced geothermal systems, CO2 sequestration, and earthquake physics
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: Essential criteria PhD in bioinformatics, computational biology, or a related discipline Extensive experience and expertise in applying/developing and training deep learning models on biological, chemical or
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highly skilled Postdoctoral Fellow with a proven dual‑mode research profile capable of independently performing laboratory experiments and coding predictive AI models in Python to forecast biomaterial
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, FDM, and/or FVM will serve you well as you develop advanced numerical methodologies and software frameworks for the simulation of fluid-structure interactions of magnetoactive polymers, contributing
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Minimum qualification of a Master’s degree, with a PhD preferred, and demonstrated expertise in coral reef data analysis, field studies, and coral reef simulation modelling. Coding experience in Matlab or R
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and development activities in the fields of atomistic simulations, including density functional theory, machine learning, and molecular dynamics. The work involves theoretical and experimental research
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of mathematical models and simulation of industrial Heat Exchanger to be used and deployed within the GEOFLEXHEAT project. This project, coordinated by Brunel, will support all the cross activities occurring during
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or related areas. Title: Modelling and simulation of the electricity transmission grid for long-term energy scenarios in the European context EU EnVis-2060: analysis of operational feasibility and
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a Horizon Europe funded research project which launched in March 2025 and is coordinated by Utrecht University. It brings together 24 partners from a wide-range of disciplines in future climate model