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federated learning SOTA algorithms. Knowledge of distributed optimization and consensus algorithms. Knowledge of large models and hyper-parameter optimization. Knowledge of high-performance computing and its
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and physical processes relevant to ocean circulation and dynamics. Experience with high-performance computing, parallel numerical models, large scientific datasets, scientific programming, and the
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learning, high-performance and parallel computing, or scientific simulation; Effective oral and written communication skills; Ability to work well independently as well as in a collaborative team
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collaborate more widely with both the project team and parallel projects the LJMU team is working on linked to sustainable management of UK lowland peat agricultural land. As such, being a strong team
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: Strong candidates may also have experience with: Large-scale neuroimaging datasets. GPU-based model training and distributed computing. Brain connectivity modeling, representational analysis, or
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Your privacy, your choice We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media, as
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. Develop reconstruction, simulation, and analysis software using modern C++, Python, ROOT, and HPC/Grid computing resources for processing multi-petabyte experimental datasets. Develop and apply state-of
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and high-performance computing to build and run advanced numerical models, carry out simulations and analyse results in a systematic and transparent way. Depending on ongoing projects, you may be
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-Independent Acquisition (DIA), Parallel Reaction Monitoring (PRM), Quantitative proteomics workflows, Data normalization and quality control strategies, Strong experience in processing and interpreting
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supercomputer architectures Research Software Engineering for massively parallel molecular and coupling software (especially Is1 mardyn, MaMiCo) Supporting the professorship HPC & Data Science in research