PhD Candidate in Mathematical Foundations of Machine Learning for Sequential Data Norwegian University of Science and Technology in Norway

The Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) in Trondheim invites applications for a PhD position in the mathematical foundations of machine learning for sequential data. This opportunity is part of the national SURE-AI project, a major Norwegian initiative to advance artificial intelligence research and innovation. The successful candidate will work under the supervision of Professor Kurusch Ebrahimi-Fard, focusing on developing mathematical and computational methods based on path signatures, stochastic analysis, and related algebraic and analytic structures for modeling complex sequential data.

The research will explore the use of path signatures—tools from stochastic integration and rough path theory—to represent time-dependent data, with applications in machine learning and data science. The project aims to combine these mathematical representations with concepts from physics-inspired machine learning, including statistical physics, dynamical systems, and stochastic processes, to design robust, interpretable, and mathematically principled learning methods. Emphasis will be placed on learning from noisy, high-dimensional, and irregular data, and exploring connections to quantum-inspired or hybrid computational approaches. Potential applications include time-series analysis, dynamical system modeling, and the study of stochastic phenomena.

The position is a three-year, full-time PhD fellowship, with the possibility of a 6–12 month extension for teaching duties. The main workplace is in Trondheim, Norway. The SURE-AI project is a national AI center funded by the Research Council of Norway, involving 19 Norwegian partners and 15 international institutions, and aims to develop new algorithms for inference and decision-making in AI, with a focus on efficiency, transparency, and ethical alignment.

Eligibility: Applicants must hold a relevant Master's degree in mathematics or equivalent (five-year Norwegian programme, 120 credits at Master's level). Current Master's students may apply if the degree is completed before starting the position (no later than 30 October 2026). A strong academic background with an average grade of B or better (NTNU scale) is required. Admission to the faculty's Doctoral Programme is mandatory. For integrated PhD, MSc grades after the fourth year must be B or better, and Bachelor degree must be C or better. Good oral and written English skills are required. Preferred qualifications include knowledge of stochastic analysis, advanced algebra or algebraic topology, and experience in machine learning. A research-oriented master's thesis is expected.

Funding: The position offers a gross annual salary of NOK 550,800 (PhD Candidate, code 1017), with a 2% statutory contribution to the State Pension Fund. Integrated PhD candidates start at NOK 490,000 per annum. The position is conditional on external funding and includes employee benefits, career guidance, and access to Norwegian language training at a basic level.

Application: Applications must be submitted electronically via Jobbnorge.no, including all required documents (transcripts, diplomas, CV, motivation letter, and supporting materials). Only applications received by the deadline will be considered. For further information, visit the SURE-AI project website or the NTNU PhD programme page.

NTNU is committed to diversity and inclusion, and encourages applications from all qualified candidates regardless of gender, background, or career interruptions. The university offers a vibrant research environment, excellent employee benefits, and a high quality of life in Trondheim.