Scientific computing

…Research areas include scientific computing, mathematical physics, geophysical fluid dynamics, dynamical systems, mathematical biology and scientific machine learning. Applicants should identify prospective faculty supervisors and submit a cover letter, CV, one-page research statement and three references. The stated application deadline is 20 Novemb

…inference, computational biophysics or scientific computing at the Center for Computational Mathematics. Fellows receive mentoring, computing resources and a travel/research budget, contribute to publications and potentially open-source software, and may mentor summer students. Appointments initially last two years and may be renewed for a third based on performance. The anticipated start is July–October 2027. This is a New York City onsite position allowing one day per week working from home.

…include AI-based mathematical methods, scientific computing, probability, optimization and theory with substantial computational content. The role combines an active research programme with undergraduate and graduate teaching and interdisciplinary collaboration. The advertised salary range is USD 90,000–135,000. Submit a cover letter, CV, research and teaching statements, a generative-AI-use statement and four references, including one on teaching. Applications received by

…areas include differential equations, scientific computing, optimization, nonlinear dynamics, mathematical physics, network science and multiscale simulation. Faculty teach, supervise students, seek research funding and contribute to curriculum development. English is the working language. Applications received before 1 November 2026 receive full consideration; the search continues until filled.

Develop AI-based software tools for materials research and scientific computing. The work explores how foundation models and generated code can improve research workflows and computational methods.

The training school develops three foundations for AI in scientific computing. Daniel Bartl introduces statistical learning through high-dimensional probability, explaining how the geometry and complexity of function classes govern generalization. Stanislav Budzinskiy covers floating-point errors, deterministic and stochastic rounding, matrix multiplication, mixed precision and conditioning in neural-network training and inference. Maximilian Herde introduces neural operators and foundation models for partial differential equations, studying pretraining, scaling and transfer to new physical pr