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Field
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between predictive performance, computational efficiency and scalability, using high-performance and cloud computing environments. Depending on the agreed research direction, you may also explore hybrid
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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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Integrated optics is currently experiencing unprecedented growth, driven largely by the rapid expansion of artificial intelligence (AI), cloud computing, and high-performance data processing
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into insight). To tackle this, the research blends ideas from knowledge graphs, stream processing, database theory, logic, edge and cloud computing, and Artificial Intelligence into a single coherent framework
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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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atmospheric circulation, cloud formation, and heat transport in water world atmospheres. Interpret observations of water worlds by comparing atmospheric simulations with measured spectra. Assess the climatic
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software development for robotics or mobile sensing applications. Familiarity with Linux-based development, ROS, sensor integration, real-time data processing, point-cloud processing, 3D reconstruction
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Change Adaptation (CCA) simulations and activities in urban environments. The PhD project should leverage multi-scale geospatial data (remote sensing/aerial imagery, point clouds, maps, etc.) and GeoAI