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Field
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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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sequencing (snRNA-seq) and spatial transcriptomics experiments on post-mortem human brain tissue and animal models. Sample processing. Bioinformatic analysis of the data generated in R and/or Python
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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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, Computer Science, Mathematics), preferably with a focus on fluid mechanics and numerical methods, with some programming experience (Python/C++). The applicant should be self-motivated, but able to work as part of a
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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