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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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are still present. This PhD research aims to develop new and efficient AI-based solutions for processing LiDAR data (2D/raster and 3D/point clouds) and improve the detection of sub-canopy archaeological
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SINTEF. The project aims to develop an automated high-throughput platform for physiologically relevant cell research by combining robotics, microfluidics, advanced sensors, cloud-connected software, and
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relevant cell research by combining robotics, microfluidics, advanced sensors, cloud-connected software, and artificial intelligence. The successful candidate will become part of a multidisciplinary
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ingredients, a process that is traditionally slow because each substrate–strain combination behaves differently. By applying machine learning to historical experimental data, we can predict high‑potential
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largely by the rapid expansion of artificial intelligence (AI), cloud computing, and high-performance data processing applications. As AI models continue to increase in size and computational complexity
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underlying digital dependencies unaddressed. Even with clear legal frameworks, the dependencies that Norwegian organizations develop on global platform companies, cloud providers, and proprietary software
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, environmental impact, power, security and ownership. UNDER THE CLOUD investigates how different actors understand, justify, or contest data centre development with a focus on the knowledge practices
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and transparency for different stakeholders? How can privacy-preserving mechanisms and AI-assisted data engineering support secure and efficient data sharing while fostering trust and responsible data
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conduct world-class applied research. We change and make a difference. Do you want to become one of us? The Department of Computer Science (DIDA) is one of three departments at the Faculty of Computing