Sort by
Refine Your Search
-
extracted from multi-spectral remote sensing products and dendrochronology data This position is part of the doctoral training unit FORFUS (https://www.list.lu/en/research/project/forfus ), associated with
-
order to better understand, explain and advance society and environment we live in. Your role The PhD position is embedded within the MICRO-PATH Doctoral Training Programme, funded by the Luxembourg
-
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
-
of sustainable technologies that positively impact society. For more information, please visit our website: www.uni.lu/snt-en/research-groups/finatrax/ The candidate will be enrolled in the PhD program in
-
: · Processing multi- and hyperspectral satellite and drone data · Collection of field data on relevant forest traits and laboratory analysis · Forest radiative transfer modelling · Hybrid
-
will also be enrolled at the University of Luxembourg as a PhD Candidate. This position is part of the doctoral training unit FORFUS (https://www.list.lu/en/research/project/forfus ), associated with
-
-assisted simulation framework by providing accurate high-fidelity numerical data for training and validation of surrogate models for multi-disciplinary design and optimization. · Participating in
-
developed using synthetic experiments through soil-canopy reflectance, thermal infrared, & surface energy balance modelling (e.g., SCOPE). Model-data integration methods will be used to derive LUE-WUE and gc
-
attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security
-
attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security