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Field
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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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at least one of the following fields: approximation of neural networks, convergence of Langevin/MC sampling, analysis and algorithm design of interacting particle systems, operator learning, regularity
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for Studies of the Universe and Particles (QUP) is a World Premier International Research Center Initiative (WPI) promoted by the Japanese government and hosted by the High Energy Accelerator Research
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programming languages. Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation. Familiarity with machine-learning or deep-learning methods for medical-image
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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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comprehensive literature review on food rheology, texture analysis, particle analysis, image analysis and the IDDSI framework. Design and execute experiments to develop new instrumental texture analysis methods
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with advanced analytical instrumentation such as HPLC, texture analyzers, FT-IR spectrometers, zeta potential and particle size analyzers, differential scanning calorimeters, and supercritical CO2
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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