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Field
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statistical analyses using Stata and Python. Review relevant academic literature and summarize findings. Prepare tables, figures, and documentation for research papers and presentations. Support other research
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of software pipelines in Python. Where to apply Website https://sede.uvigo.gal/public/catalog-detail/27660208 Requirements Research FieldEngineering » OtherEducation LevelBachelor Degree or equivalent Specific
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Description Electrophysiology and neuroanatomy experiments in the central nervous system of lampreys. Analysis of electrophysiological data. Acquisition of behavioral videos and analysis using Python tools
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partners. Training & environment You’ll gain deep skills in hypersonic flows, AI for PDEs, data assimilation, and reproducible HPC workflows (Python/C++/PyTorch/JAX). You’ll be supported with paper writing
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. • be located at the agreed project location(s) and, if required, comply with the university’s external enrolment procedures. Selection criteria Skillset: Proficient in Python, machine learning, and
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Physics. Skills in Python and/or MATLAB programming are highly desirable, as are skills in both verbal and written communication in English.The PhD programme will be supervised by Dr Finnigan Illsley-Kemp
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samples. All computational methods and algorithms will be implemented as part of the python based MetaboLabPy platform (https://doi.org/10.3390/metabo15010048 , https://github.com/ludwigc/metabolabpy
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: Comparative transcriptomics, orthology inference, positive selection detection, protein domain analysis, phylogenetic comparative methods Computational skills: UNIX/Linux, HPC computing, R, Python You will gain
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programming skills in Python/MATLAB, and an interest in digital twin technologies, cybersecurity and machine learning. Entry Requirements Acceptable first degree: Computer Science or related disciplines
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cell and spectroscopic analysers. Programming (e.g., R, Python) and machine learning for advanced atmospheric time-series analyses. Skills for presenting research at conferences and writing peer-reviewed