49 parallel-computing-numerical-methods "Simons Foundation" PhD positions in Luxembourg
Sort by
Refine Your Search
-
apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
-
the development of both, the quantum internet and distributed quantum computing. The objectives of this PhD thesis project are: (a) Demonstrate spin-photon entanglement with single colour centres in silicon carbide
-
machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
-
computer science, engineering, information systems, economics, management, law, and other fields, united in pursuit of sustainable technologies that positively impact society. For more information, please visit our
-
into this material and support tailoring its properties. For this, you will: Contribute to method development for ultra-fast MLIPs (Xie et al., npj Comput. Mater., 2023) Develop realistic MD simulation protocols
-
society. For more information, please visit our website: https://www.uni.lu/snt-en/research-groups/finatrax/ The person will pursue a Ph.D. degree (Doctorate) in computer science and information system
-
Pathogenesis in the age of the microbiome (MICRO-PATH; https://micro-path.uni.lu ) is a highly competitive, interdisciplinary, research-intensive PhD training programme, supported by the PRIDE
-
associated threats. The research project of the PhD student will thus focus on defining methods to track, monitor, and manage the use of GenAI. While this can rely on recentely proposed telemetry framework
-
) - Three-dimensional conformally flat Lorentzian manifolds through experimentation (Karin Melnick) - Representation-theoretic methods in algebraic geometry (Karin Melnick & Pieter Belmans) - Computational
-
computational models and data analysis code to process large, multimodal behavioral datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning