Topic: Scientific machine learning

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.

The Simons Foundation invites two-year research proposals that use Empire AI's multinode GPU clusters to address fundamental questions in computational astrophysics and physics, computational biology, mathematics, neuroscience or computational physical chemistry. Proposals must develop and use foundational machine-learning models for the scientific problem; data curation may be considered only when the resulting data are used to solve scientific problems within the same proposal. The programme expects to fund six to twelve awards from a total budget of six million US dollars.

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.

For decades dynamical systems theory played a pivotal role in theoretical and computational neuroscience, as it links biophysical and biochemical processes to neural computation. In fact, dynamical systems are computationally universal. Rather than hand-crafting computational theories of neural function based on dynamical 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