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the fields of differential equations and/or stochastic processes, as well as computational approaches and/or statistical methods of data analysis. You can find more information about our research area and our
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are particularly interested in candidates with experience in one or several of the following areas: - Inference of stochastics dynamics - Numerical analysis / Mathematical analysis / modeling for chemistry or
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part of the Virgo Collaboration at the European Gravitational Observatory (EGO) and has been active so far in searches for ultra-light dark matter, anisotropic stochastic GW background, gravitational
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” (“Stochastic Operations Research”), consisting of one two-hour lecture per week together with associated exercise classes, preparation and administration; contribute to other courses in the area of probability
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of conduct, and a statement by the Lab Director's office can be found here: https://www.ornl.gov/content/research-integrity Basic Qualifications: Ph.D. in mathematics, engineering, data/computational science
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(primarily ODEs, but also PDEs and stochastic models) of viral replication and immune processes. Implement simulations and perform computational experiments using high-level programming languages (e.g., Python
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responsibilities described above. Within the department, research areas include optimization, stochastics, statistics, machine learning, artificial intelligence, and analytics, with applications to healthcare
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Research Fellows in applied mathematics which can be found at: https://www.jobs.ox.ac.uk/ using vacancy ID : 182821 Two Glasstone Fellowships are available and all applications for the Hooke and Titchmarsh
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probability, stochastic modeling, scientific computing, mathematical optimization and control, numerical analysis, and relevant adjacent fields Appl Deadline: 2025/11/10 11:59PM (posted 2025/09/24, listed
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possible including algebra, analysis, combinatorics, control theory, dynamical systems, geometry, numerical analysis, probability, statistics, stochastic analysis/control, partial differential equations, set