Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
University of Science and Technology (NTNU) has a vacant position as PhD candidate in the field of machine learning for materials science. Your immediate leader will be the Head of Department. About the
-
data-driven learning and which should remain within structured optimization. In line with AID’s research areas, the project will emphasize knowledge embedding, uncertainty representation, risk-aware
-
selection criteria Knowledge of sensors and measurement techniques Experience from the industry providing sails for merchant ships Experience with Computational Fluid Mechanics Knowledge of machine learning
-
for recruitment positions for general criteria for the position. Preferred selection criteria Good oral and written presentation skills in Norwegian/Scandinavian equivalent level or acquire them during the course
-
25th September 2026 Languages English English English The Department of Marine Technology has a vacancy for a PhD Candidate in Deep Learning Enhanced FSI analysis of Modular Floating Structures PhD
-
in one or more of the following research areas is desirable: geometric numerical integration, structure preserving deep learning, stochastic differential equations, generative AI, numerical
-
for recruitment positions for general criteria for the position. Preferred selection criteria Good oral and written presentation skills in Norwegian/Scandinavian equivalent level or acquire them during the course
-
1st October 2026 Languages English English English The Department of Electronic Systems has a vacancy for a PhD Candidate in Machine Learning & Signal Processing for Industrial Applications Apply
-
, structure preserving deep learning, stochastic differential equations, generative AI, numerical optimization. Strong programming skills (Python, Julia, Jax). Experience with numerical optimization is also
-
SINTEF, Equinor, and Total. The main objectives of the project include the development and the integration of signal processing and machine learning methodologies aiming to improve flow assurance via field