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candidate with a strong background in some aspect of numerical analysis for PDEs and an interest in scientific machine learning and probabilistic methods, who enjoys working in collaborative inter
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extrapolation based on the principle of geometric similarity and the choice of an invariant. Using this method therefore requires precise knowledge of the phenomena involved. As concerns numerical methods, while
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involves mathematical modeling and numerical simulation, but also the analysis of experimental datasets for model validation. Your Profile: A Masters degree with a strong academic background in physics
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design scalable numerical methods for quantum master equations, implement high-performance simulations, and help build open-source tools for large-scale spin-system modeling. By improving our ability
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equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our
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related field Strong background in numerical methods and machine learning Proficiency in at least one programming language (Python, Julia, C++, …) Good analytical skills Good organizational skills and
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Tensorflow or Pytorch is advantageous Experience in numerical methods for partial differential equations is beneficial Effective communication skills and an interest in contributing to a highly international
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for an additional year. This fellowship will support an emerging scholar whose work uses interdisciplinary methods grounded in Art History to chart new pathways for the field. Area of expertise is open but should
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
and enjoy exclusive perks for numerous retail, restaurant and performing arts discounts, savings on local child care centers and special rates on select campus events. UNC-Chapel Hill offers full-time
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methodical, rigorous, reactive, and attentive, with a strong work ethic and high integrity. He/she will already have experience with human stem cells, neuronal differentiation, as well as 3D culture, organoids