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build reliable, reproducible data flows for large EO datasets and workflows Lead performance engineering (parallelization, optimization, benchmarking) for adaptation and inference at scale Work closely
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. Understanding of massive high performance parallel storage and methodologies. Expert knowledge of Unix/Linux systems administration, including all aspects of management, monitoring, performance analysis, and
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. The successful candidate will play a central role in SLAS’s Parallel Degree Programme (PDP) in partnership with the University of London (UoL), particularly through teaching contributions
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cultivation of the beneficial microbes of the human microbiome. This work involves the use of parallel plating in microtiter wells of combinations of nutrients and bacterial cells, using multiplate readers
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sintering press with selected copper pastes, followed by detailed characterization of the resulting interfaces in terms of porosity, thermal and mechanical integrity. In parallel, simulation models will be
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parallel, forward genetics will be used to identify novel regulators of the fungal cell wall, and mutants will be studied with the methods above to find potential antimicrobial targets. We will provide
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to the staff, and serves as primary backup for bi-weekly and semi-monthly payrolls. Performs work related to Workday implementation and upgrades, user acceptance, and parallel testing. Brings experience, shares
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signal processing and/or survey datasets. ML & AI techniques and applications. HPC and orchestration of scientific data processing workflows. Parallel computing (GPU & CPU). good software engineering
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programming, parallel computing, and bioinformatic and statistical genetic software packages. Experience in working with genomic and proteomic data available for large extended pedigrees. License
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expertise in high-performance computing (HPC), parallel computing frameworks (such as MPI, OpenMP) and GPU acceleration. Proven experience administering, configuring, and optimising HPC clusters and GPU