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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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of biological or process systems. experience applying AI or machine learning to scientific problems, with proficiency in Python or equivalent programming tools. experience in experimental design and validating
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statistical software including Stata, R or SAS, python. Have an emerging publication track record with high impact publication(s) within biostatistics, cancer and health services research or another related
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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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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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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