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Field
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. Utilizing a combination of experimental and computational approaches, we develop and characterize novel functional materials and devices driven by robust nanoscale quantum effects. We are currently seeking a
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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students on data analysis and detector projects. 40% Instruction of introductory physics lecture section 25% Core reconstruction and simulation software and computing development 15% Construction
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programmes: Chemistry; Physics; Electrical, Electronic & Computer Engineering; Chemical Engineering; Mechanical Engineering and Brewing & Distilling. In the 2019 National Student Survey Heriot-Watt University
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Engineering, Computer Science, Applied Mathematics, or related fields Strong background in control systems, machine learning, and scientific computing Programming proficiency and experience with simulation
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to identifying and interpreting trends in regional-to-local scale signals and extreme events. Large ensembles of climate simulations are a key tool in such applications, but the computational cost of producing
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climate change are far-reaching, particularly when it comes to identifying and interpreting trends in regional-to-local scale signals and extreme events. Large ensembles of climate simulations are a key
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to address critical computational challenges in simulating the physical dynamics of millions of lakes worldwide. The successful candidate will create innovative solutions that significantly enhance large-scale
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(admission-completion) in the master's study program must be specified. A complete list of publications (if relevant). For applicants to the PhD fellowship: a copy of the master thesis. For applicants
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methods (LBM). For fluid simulations, we utilize the high-performance LBM framework waLBerla, predominantly written in C++, but increasingly adapted for GPU computations through automatic code generation