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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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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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to prioritize multiple tasks and meet deadlines. Experience using Adobe Creative Cloud applications. Familiarity with Canvas learning management system. Experience with Concur and/or travel and expense
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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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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
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slowly, or act quickly but lack robust planning. Frontier models typically depend on heavy compute, cloud inference or controlled settings, limiting real-world use under compute, latency and energy
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, cloud formation, and heat transport in water world atmospheres. Interpret observations of water worlds by comparing atmospheric simulations with measured spectra. Assess the climatic conditions and
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is related to clouds, radiation, and atmospheric circulation. Climate models are at the core of our research, in particular models that simulate global weather and climate at kilometer-scale resolution