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Field
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EO solutions. We offer: a stimulating multinational, interdisciplinary and open work environment; access to high-performance computing infrastructure and unparalleled EO and technology expertise
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developmental, ageing or neuropsychiatric cohorts, including longitudinal or large-scale population datasets. · High-performance computing, reproducible workflow development, version control and
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. Strong quantitative and analytical skills in working with large-scale geospatial datasets and socio-economic databases. Proficiency in high-performance computing (HPC) environments and scripting (e.g
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downstream statistical inference. · Developing and implementing computationally intensive algorithms using high-performance computing (HPC) clusters. · Managing and analyzing multiple large
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, providing access to national data networks, high-performance computing clusters, and clinical interdisciplinary collaborations across UT Austin. The applicants will join a collaborative research
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, variational inference, transport methods, etc.). • Experience with inverse problems or uncertainty quantification. • Experience with scientific machine learning. • Experience with high-performance
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independently Confidence with computational biology including use of programming languages such as R, python and some familiarity with high performance computing Excellent communication and interpersonal
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Having rich skills in coding in Fortran/C/C++ and high-performance computing Good knowledge in fluid mechanics, heat and mass transfer as well as surface phenomena. With good record in publishing research
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analysis and patient or gene prioritisation. Working within research high-performance computing (HPC) environments, the Research Fellow will execute defined research workstreams, contribute to analytical
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-performance computing. In addition, Dr. Topp is spearheading the development of a new X-ray imaging facility with focus on non-destructive, high-resolution analysis of root growth and soil interactions