SINTEF projects

A new software developed by SINTEF makes it more efficient to plan daily airport operations, reducing delays, aircraft idling and recovery time.

The central R&D challenges within this project focus on the use of machine learning techniques and cloud-based services to deliver more user-friendly and robust 3D camera solutions.

The AI4DI mission addressed bringing AI from the cloud to the edge and making Europe a leader in silicon-born AI by advancing Moore's law and accelerating edge processing adaption in different industries through reference demonstrators within the...

The aim of TAPI (Towards Autonomy in Process Industries) is to move Norwegian land-based process industries towards more autonomous operations by exploring the intersection between machine learning (ML) and more traditional model-based control...

SINTEF has developed Mobility-as-a-Service adapted to scattered rural areas. The main idea is a system for transport planning that coordinates needs for passenger and goods transport in order to use transport resources optimally.

StraTi is a short exploratory research project for extending SINTEF's existing train management tools in order to support Strategic Timetabling.

The OceanEye project targets to develop sensing, perception, and data management for Uncrewed Aerial Systems (UAS) operating at sea scanning for small floating objects on the sea surface.

Develop a compact and easy-to-use retinal camera for early screening of diabetic retinopathy.

The FeedCarrier project will develop an automatic feeding system that analyses, fills, and distributes feed fully automatically, thereby reducing manual labour and improving safety for the farmer as well as enabling precise control over the timing...

The Digital Worker ERA will focus on exposure, physical and organisational factors for workers in the petroleum industry. We will build new knowledge on real-time exposure assessment of chemical, physiological strain and the human factors elements of...

Our primary goal of the project is to develop and test an optical sensor and a prediction model of winter road surface condition

The primary objective of the project is to develop a novel cost-efficient method for tophole/non-invasive monitoring of permanently plugged wells that are cut below surface/seafloor.

Warped cardboard is a major quality issue in the production of corrugated cardboard. It reduces the productivity in the converting units, causes reduced quality on the final product, and increases waste. In UnWarp we are using machine learning to...

The primary objective of the DroneSAFE project was to develop a professional-quality cinematography solution for action and adventure sports in an affordable, safe, and easy-to-use consumer drone platform.

Our research focus on risk reduction in autonomous systems by associating Deep Learning (DL) predictions with the inherent model and data uncertainty.

The innovation project has contributed to the development of a new integrated maritime sensor platform for detection and classification of safety-critical objects around a ship. Real sensor data from a larger ship has been used as test data in the...

The subsea industry is constantly pushing towards reduced costs and increased safety in subsea inspection, maintenance and repair operations. Therefore we have established the SEAVENTION project: Autonomous subsea intervention - empowered by people...

A research focus for us is to develop Deep Learning (DL) methods for accurate detection and prediction of the 6-Degrees of Freedom (6-DoF) pose of relevant objects in the challenging underwater environment.