Sort by
Refine Your Search
-
Category
-
Employer
-
Field
-
UiO/Anders Lien 20th October 2026 Languages English English English Join the University of Oslo for a PhD in Biomaterials! PhD Position – AI-Driven Multimodal Analysis and Predictive Modelling
-
AI-driven quantitative analysis and predictive modelling of biomaterials, tissues, and regenerative constructs. The project combines advanced 2D and 3D bioimaging, including micro/nanoCT, confocal
-
‑violation. For example, the candidate will do some of the tasks including: AI-driven Link Adaptation and MAC control – develop AI-based mechanism that proactively anticipate and respond to rapidly changing
-
. The research will focus on developing hybrid learning–control architectures that integrate model-based control and planning methods with data-driven learning approaches. Potential topics include safe
-
to strengthen resilience, preparedness, and fair allocation; (2) establishing site-specific, stakeholder-led strategies; (3) creating seasonal and long-term foresight by developing and coupling a suite of models
-
of control systems theory to create a new generation of intelligent underwater robotic systems. The research will focus on developing hybrid learning–control architectures that integrate model-based control
-
for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
-
. This highly innovative project aims to develop a fully AI driven digital twin that enables real-time optimization and control of fermentation processes. The candidate will develop the digital twin for microbial
-
to investigate cascading cyber effects, cyber resilience, and advanced cyber range modelling. The research combines simulation, software development, and experimental validation in the Norwegian Cyber Range
-
Contributions RO1: Develop methods for modelling dependencies and cascading cyber effects in interconnected systems. The research will investigate approaches for representing how cyber incidents propagate across