Sort by
Refine Your Search
-
Category
-
Country
-
Employer
- Fondazione Bruno Kessler
- ETH Zürich
- Eindhoven University of Technology (TU/e)
- Monash University
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- The University of Newcastle
- University of Vienna
- Blekinge Institute of Technology
- CNRS
- Durham University
- Empa
- Forschungszentrum Jülich
- KU LEUVEN
- Norwegian Institute of Bioeconomy Research
- Technical University Of Denmark
- UNIVERSITY OF VIENNA
- University of Copenhagen
- University of Nottingham
- University of Southern Denmark (SDU)
- Wageningen University & Research
- 11 more »
- « less
-
Field
-
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
-
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
-
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
-
Integrated optics is currently experiencing unprecedented growth, driven largely by the rapid expansion of artificial intelligence (AI), cloud computing, and high-performance data processing
-
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
-
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
-
relevant cell research by combining robotics, microfluidics, advanced sensors, cloud-connected software, and artificial intelligence. The successful candidate will become part of a multidisciplinary
-
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
-
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
-
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