Sort by
Refine Your Search
-
Category
-
Field
-
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
-
contact details · List of publications (and patents, if applicable) · Contact details of 2 references Please apply ONLINE formally through the HR system. Applications by email will not be
-
training unit: https://www.list.lu/en/research/project/forfus Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? Your PhD work will focus on outdoor forest
-
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
-
susceptible to SM, VWC, and atmospheric delay. As a result, the objective of this PhD project is to develop models able to fuse backscattering and phase information to estimate SM and VWC more accurately. The
-
collaboration with national and international partners. The PhD candidate will be supported through a FNRS-Televie funded project focusing on the crosstalk between m6A RNA methylation pathway and metabolic/lipid
-
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
-
spin-photon entanglement. With this milestone demonstration, we strive to kickstart subsequent developments towards a quantum internet architecture. The objective of this PhD thesis is to demonstrate
-
· PhD in Aerospace Engineering, Mechanical Engineering, Applied Physics, or a closely related discipline. · Strong academic background in computational mechanics, fluid dynamics, electromagnetics
-
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