Scientific machine learning

…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

Two positions with Yingda Cheng and Daniel Appelo connect numerical analysis and scientific machine learning with fusion plasmas, quantum devices and high-performance computing. Appointments in Burnaby last two years, potentially three, with a January–September 2027 start and up to one undergraduate course annually. Salary is CAD 65,000–70,000 plus benefits. Applicants need a relevant doctorate no more than three years before starting and strong research and programming skills. Submit a cover letter, CV, research plan, two papers, work-status information and three references through MathJobs.

…models, scalable algorithms, scientific software, high-performance computing and scientific machine learning. It takes place in Pittsburgh on 22–26 February 2027. Travel-support applications are open until 30 November 2026. Abstract deadlines have passed; registration is scheduled to open in December 2026. The meeting is co-located with ACDA27 and the International Meshing Roundtable.

One position in Takaharu Yaguchi’s Computational Physics Machine Learning Team at RIKEN AIP develops reliable scientific machine learning. The team studies algorithms that respect physical laws, mathematical analysis and models for accelerating simulations. The appointee will conduct research, publish at leading venues and help guide students and technical staff. The workplace is Kyushu University’s Ito Campus in Fukuoka. The appointment level depends on experience. Recruitment continues until the position is filled; applicants start through the HR inquiry link on the vacancy page.

…This models operator learning in scientific machine learning as well as computational algorithms. Randomized SVD provides strong guarantees for low-rank targets; analogous results for sparse and hierarchical structures are less developed. The talk presents progress on efficient algorithms for these classes and a broader complexity theory. Joint work with Noah Amsel, Pratyush Avi, Tyler Chen, Prathamesh Dharangutte, Chinmay Hegde, Feyza Duman Keles,

…systems, recent developments in scientific machine learning (ML) and AI suggest that we may be able to infer such dynamical-computational models directly from neurophysiological and behavioral observations. This is called dynamical systems reconstruction (DSR), the learning o