Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
Experience with deep learning for image analysis and/or medical image processing Knowledge of self-supervised learning, representation learning, and/or generative models Experience with multimodal machine
-
. Experience with control and synchronization of high-speed imaging and lighting systems. Experience with image post-processing and data extraction. Personal characteristics To complete a doctoral degree (PhD
-
imaging and lighting systems. Experience with image post-processing and data extraction. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Ability to work
-
(viability, proliferation, outgrowth, and invasion assays) is desirable. Experience with, or interest in, machine learning for the analysis of microscopy data and a strong ability to collaborate with
-
» Programming Engineering » Computer engineering Computer science Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 26 Apr 2026 - 23:59 (Europe/Oslo) Country Norway Type
-
machine-learning methods to enhance predictive capability and enable adaptive process control. Experimental work will include laboratory- and industrial-scale forming trials, supported by comprehensive
-
systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI outputs in a known structure. This
-
(ph.d.) in artistic development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning
-
Computer science Engineering » Computer engineering Technology » Information technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 10 Feb 2026 - 23:59 (Europe/Oslo
-
selection criteria Experience with machine learning or other relevant AI technologies Experience with condition monitoring, preferably within maritime domains Knowledge of ship machinery and systems Good oral