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reactions, catalyst surfaces and interfaces, reaction mechanisms, activity and selectivity develop reproducible atomistic simulation and high-throughput workflows using Python, ASE and relevant DFT software
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learning or a related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: implementation of signal processing
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in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: implementation of signal processing systems, data collection
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or equivalent approaches. Automated experimental testing, instrument control or data acquisition using tools such as LabVIEW, Python or MATLAB. Pre-employment checks and declarations Your employment is
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arising from finite fields, projective and polar spaces, and related structures. Apply computational methods (e.g. Magma, GAP, SageMath, Python) to test conjectures and explore new mathematical directions
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or equivalent approaches. Automated experimental testing, instrument control or data acquisition using tools such as LabVIEW, Python or MATLAB. Pre-employment checks and declarations Your employment is
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using tools such as LabVIEW, Python or MATLAB. Pre-employment checks and declarations Your employment is conditional upon the successful completion of all pre-employment or background checks required
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science, statistics, biomedical engineering or a related field strong programming skills in R or Python experience working with spatial omics data, histopathological data, or related large-scale biological datasets
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discipline strong programming skills in R or Python experience developing or applying methods to complex or large-scale datasets ability to work both independently and collaboratively within
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. Advanced quantitative programming and data-analysis skills, preferably in Python, together with the ability to develop transparent, robust and reproducible modelling workflows. Experience working with large