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Field
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the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use similar techniques to make a statistical inference of the population of subhaloes by
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expertise in discrete choice modelling, statistical modeling, traffic simulation, machine learning, and optimization techniques. • Strong expertise in discrete choice modelling, statistical modelling, and
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partial classifications assigned in each evaluation criterion, and considering the weighting factor given to each parameter. In this process abstentions are not allowed. In the event of a tie among
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team and supported by cutting-edge HPC and GPU infrastructure, you will contribute to internationally leading research, publish in high-impact journals and present your work at major scientific
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streams with perturbation signatures and fit these. For these fits, we will explore the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use
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population and comparative genomics to examine genetic diversity, selection, pangenome relationships, and functional conservation. You will also develop reproducible GPU- and CPU-based high-performance
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advanced finite element simulations during water pumping. Key Responsibilities: Geotechnical Physical Modeling: Plan and execute 1-g and geotechnical centrifuge tests (utilizing a 40 g-ton centrifuge
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. Demonstrated experience with training and calibrating other large-scale complex models. Demonstrated ability to pretrain large-scale models from scratch, including distributed multi-GPU training. Demonstrated
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consisting on the sum of the partial classifications assigned in each evaluation criterion, and considering the weighting factor given to each parameter. In this process abstentions are not allowed. In
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, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology. This position is a prestigious Empire AI Fellowship