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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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platform enables us to test hundreds of different conditions in parallel and assess their impacts on human immune responses, such as antibody production. We routinely work with industry partners to exploit
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. Qualifications: • A PhD in applied mathematics, statistics, electrical engineering or computational sciences completed within the past 5 years (or soon to be completed) • Experience in numerical analysis and
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variety of conditions, in particular seeking evidence for Fermi acceleration of electrons under low-to-moderate guide magnetic field and parallel electric field energization under large guide magnetic field
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Education At the time of hiring, a PhD in Solid state Physics, Theoretical Chemistry, Computational Materials Science, or related fields. Required Experience Strong foundation in Quantum Mechanics and
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transmission modeling, statistical modeling, spatial data analysis, and cost-effectiveness analysis. In parallel, we conduct research on vaccine-preventable infections, developing and evaluating predictive
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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Lab researches on a variety of computer systems topics including HPC resilience, data center power management, large-scale job scheduling and performance tuning, parallel storage systems and scientific
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Laboratories (LTS5 ), Mr Jackson at the UoE Parallel Computing Centre (EPCC ), Prof. Smirnov from the South African Radio Astronomy Observatory (SARAO ), Dr Akiyama from MIT Haystack Observatory (Haystack ), Dr
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-of-the-art foundation models and large vision-language models. Experience in large-scale deep learning systems and/or large foundation model, and the ability to train models using GPU/TPU parallelization