33 model-checking PhD positions at NTNU Norwegian University of Science and Technology
Sort by
Refine Your Search
-
materials, including porous dimension, building ingredients, wettability, etc., and their interactions with water and gas species include CO2, Hydrogen and methane. Using atomistic modeling, the study will
-
, and deployability of deep learning models on resource-constrained edge platforms. The PhD candidate will collaborate closely with international project partners and contribute to advancing next
-
stem-cell-based model system recently established in the group. We are looking for a highly motivated PhD candidate with some experience in working with stem-cell based models, CRISPR-based gene editing
-
. You will explore how emerging AI technologies—foundation models, generative design tools, agent platforms, reasoning engines, and reinforcement learning—can be adapted and extended for maritime design
-
candidates within Modelling Strength and Failure in Recycled AluminiumAlloys funded through the Centre for Research-based Innovation SFI FAST – Future Aluminium Structures. The positions are linked
-
models, and collaboration mechanisms to ensure efficient, reliable, and sustainable use of shared offshore energy resources. The successful candidates will be a part of a dynamic and internationally
-
. Digital twin technologies offer a promising approach for modelling and monitoring such complex systems. However, the ongoing energy transition also introduces significant uncertainty due to fluctuating
-
industry in both acoustics and solar energy. You will gain expertise in experimental acoustics, advanced numerical modelling, sustainable building technologies and performance optimization. Are you motivated
-
on the combination of Reinforcement Learning (RL) and Model Predictive Control (MPC). It will build up upon the work done at ITK on the topic. Several research focuses are considered: verification pathways in RLMPC
-
organizations to develop new expertise, assessment methods, and governance models capable of managing risks across the AI lifecycle. At the same time, Responsible AI principles like transparency, fairness