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Field
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of the research engineer is to set up multi-physics simulations with large supercomputers on the perimeters of the project by developing advanced pre- and post-processing, the necessary models and numerical methods
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of interpretability methods to ensure ML outputs are meaningful in scientific contexts. Preferred: Background in biomedical data, healthcare, or AI for life sciences. Experience with parallel computing. Familiarity
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of scientific computing principles and applications, including numerical methods, simulation techniques, and computational modeling. Proficiency in using version control systems like Git for collaborative
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This PhD project is at the intersection of electromagnetism, numerical methods, and high-performance parallel computing, with application towards the design and optimisation of integrated circuits
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Shifting bits: Adaptive numerical precision for GPU software in particle physics and beyond (S3.5-COM-Richmond) School of Computer Science PhD Research Project Competition Funded Students Worldwide
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provide powerful tools to improve the quality and efficiency of data-driven models. In parallel to the development of data-driven models for dynamical systems with geometric structures such as Hamiltonian
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translational research efforts. May develop an independent research program focused on genetics of susceptibility to complex disease or on development of computational methods for genetic research. Contributes
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MRI scanners with massively parallel transmit and receive technologies and multinuclear options, a number of DNP hyperpolarizers, and numerous (dozens) of other scanners and instruments placed in
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of scientific computing principles and applications, including numerical methods, simulation techniques, and computational modeling. Proficiency in using version control systems like Git for collaborative
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(multiscale, QSP, PBPK, PK-PD).Apply numerical methods, optimization, and parameter estimation to calibrate models to experimental/clinical data.Perform sensitivity and uncertainty analyses to assess robustness