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approaches into a unified digital twin framework implementation of simulation and machine-learning workflows in Python (e.g. PyTorch, Scikit-Learn, etc.) incorporation of experimental microstructure and
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data from FASTQ/BAM and genome reference datasets. Experience with comparative genomics and/or germline gene discovery. Strong programming skills in Python and/or R, with practical experience in Linux
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foundational knowledge in machine learning (deep learning, reinforcement learning, or related areas). Programming experience in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow
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Engineering Energy Engineering Process Engineering Or a closely related discipline A background in thermodynamics, energy systems, process modelling, programming, or simulation tools such as MATLAB, Python
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can derive by hand and build up to large-scale calculations. You will need a background in condensed matter physics / quantum mechanics and an interest in coding (Python); no prior experience with
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discipline A background in thermodynamics, energy systems, process modelling, programming, or simulation tools such as MATLAB, Python, Aspen Plus, TRNSYS, or EES is desirable. Funding Package UKRI tax-free
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, Computer Science, or a related field. At least two years of postdoctoral or equivalent research experience, with a strong record of peer-reviewed publications. Strong programming and computational skills in Python
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programming skills in Python and/or C/C++, with experience using machine learning frameworks such as PyTorch or TensorFlow. Familiarity with rendering, simulation, or physics-based modelling (e.g., ray tracing
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, protein purification, and/or machine learning is a clear plus. • Experience in data analysis and coding (preferably Python) • A systematic, independent working style and ability to work in a collaborative
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methods is helpful but not essential. Some programming experience, for example in Python or R, is an advantage, and applicants should be willing to develop it during the PhD. How to apply Informal enquiries