Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- NTNU Norwegian University of Science and Technology
- NTNU - Norwegian University of Science and Technology
- UiT The Arctic University of Norway
- University of Oslo
- Western Norway University of Applied Sciences
- Nansen Environmental and Remote Sensing Center
- Norwegian University of Life Sciences (NMBU)
- University of Bergen
- University of South-Eastern Norway
- University of Stavanger
-
Field
-
& Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame
-
of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish an internationally leading interdisciplinary hub that advances foundational research, responsible
-
to work on cutting-edge research at the intersection of deep learning and computer systems. The successful candidate will join an international and collaborative research environment and contribute
-
universities, research institutes, industry, public agencies, and leading global institutions. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including
-
30th April 2026 Languages English Norsk Bokmål English English PhD Fellow in Machine Learning Apply for this job See advertisement About us The Nansen Center is a Norwegian environmental research
-
complex biological systems. Research Environment & Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable
-
selection criteria Experience with AI / probabilistic AI / Machine Learning / Reinforcement Learning Experience with numerical optimization and MPC Strong programming skills (Python, C) Personal
-
representations of time‑dependent data through sequences of iterated integrals and have recently gained significant attention in machine learning and data science. The project will investigate how
-
areas: Developing and training robust machine learning surrogates to replace computationally expensive high-fidelity simulations, enabling exploration of vast design spaces. Formulating optimization
-
into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract