Sort by
Refine Your Search
-
Category
-
Employer
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- University of Oslo
- University of Bergen
- Norwegian University of Life Sciences (NMBU)
- University of South-Eastern Norway
- BI Norwegian Business School
- Nord University
- Norwegian Institute of Bioeconomy Research
- Oslo Metropolitan University
- Molde University College
- NTNU
- OsloMet
- OsloMet – Oslo Metropolitan University
- UNIS
- UiT The Arctic University of Norway
- University of Inland Norway
- jobs.ac.uk
- 8 more »
- « less
-
Field
-
, but for example: University of Ulm (Germany): Algorithms for wearable data analysis University of Manchester (UK): Mathematical modeling of hormone rhythms Qualifications and personal qualities: We
-
building sensor data, you will train AI models to recognize abnormal performance patterns, quantify quality-adjusted service life, and autonomously recommend whether a system needs maintenance, recalibration
-
and digital transitions, often referred to as the twin transitions. Across Europe, governments, industries and public institutions increasingly rely on scenarios, models, indicators, roadmaps and
-
user-informed machine learning models that incorporate geological/geotechnical priors to interpolate 3D rock mass properties between sparse data points. Architect and implement a new stope optimization
-
element simulations, blast loading models, propagation of pressure waves in the ground and soil–structure interaction analyses to establish methods for assessing the protective capacity of existing
-
infrastructure. The research will combine advanced finite element simulations, blast loading models and fluid–structure interaction analyses to establish methods for assessing the protective capacity of existing
-
Particle Fluid Dynamic (CPFD) model development. The person appointed will be affiliated with the Research Group in Energy and Environmental Technology (URGENT). Duties Complete the doctoral programme up
-
absorption in gasification of biomass. The overall approach is lab-scale experiments in combination with Computational Particle Fluid Dynamic (CPFD) model development. The person appointed will be affiliated
-
) these effects can contribute significantly to local dynamics and their contributions are often under-estimated using global models. Previous work has shown that ionospheric mesoscale flows vary at different
-
on AI-empowered asset management using drone inspections and available asset management historical data. The aim is to enhance the asset and network resilience of ports by training an AI model on ferry