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- MOHAMMED VI POLYTECHNIC UNIVERSITY
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Field
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finite element analysis, finite difference schemes, or Monte Carlo simulations. Mentoring Assist in mentoring graduate and undergraduate students in related projects. Collaborate with faculty and students
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have expertise and experience in executing density functional theory (DFT) calculations, microkinetic modeling, kinetic Monte Carlo simulations, and Machine learning methods. Minimum Qualifications: Must
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coupled nuclear engineering problems, using techniques such as (but not limited to) molecular dynamics, computational fluid dynamics, activation decay codes, kinetic Monte Carlo codes, particle transport
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transport modeling and machine protection strategies for the EIC accelerator complex. This position will focus on Monte Carlo simulations to characterize the radiation environment resulting from beam losses
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, geometric modelling, acoustic signal propagation, Monte Carlo simulation methods, decision theory, uncertainty quantification, machine learning. Applications and areas of key innovation Image analysis
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protection strategies for the EIC accelerator complex. This position will focus on Monte Carlo simulations to characterize the radiation environment resulting from beam losses and synchrotron radiation, with
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closely related field, with a strong interest in biological systems are particularly encouraged to apply. We seek candidates with expertise in some of the following areas: molecular dynamics, Monte Carlo
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, instrumentation, signal processing, biomedical optics, wearables, light-tissue interaction, statistical analysis, design of optical systems, Monte Carlo modeling. Preferred Qualifications Education: No additional
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without incurring prohibitive computational costs. Unlike standard sequential sampling methods, the project will explore batch-based active learning, prioritizing diversity and informativeness in data
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, sensing techniques, optimisation theory and algorithms, multi-modal data processing, high-performance computing, mathematical image analysis, geometric modelling, acoustic signal propagation, Monte Carlo