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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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, or optical diagnostics is desirable but not essential. Familiarity with coding or data analysis (e.g. MATLAB, Python, LabVIEW) would be an advantage. Good written and spoken English communication skills
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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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. Experience with machine learning algorithms and strong programming skills using platforms such as MATLAB, R, Bash and Python will be essential. For informal inquiries regarding the role, contact Prof Maura
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communication skills Strong analytical and mathematical skills Programming experience (e.g. Matlab, Python, C/C++,…) The following background is desirable but not essential: Foundations in Continuum Mechanics and/or
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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