We have a job opening for a two-year LiDAR Remote Sensing Scientist for ecological applications at the University of Amsterdam, The Netherlands. The person will be responsible for developing scientific workflows for ecological applications in biodiversity and ecosystem science using airborne and spaceborne LiDAR data. The person will also closely interact with computer scientists and software engineers to make the workflows and tools available in the context of a Virtual Research Environment (VRE).
We are looking for a candidate with exceptionally strong skills in LiDAR processing and ecological applications of LiDAR data in relation to biodiversity and ecosystems. The person will develop workflows using LiDAR data from multiple country-wide airborne laser scanning surveys as well as from spaceborne observations of the new Global Ecosystem Dynamics Investigation (GEDI). Ecological applications can include (but are not restricted to) species distribution or biodiversity models with LiDAR metrics, land cover and habitat classification and mapping using LiDAR with machine learning, or change detection of ecosystem structure using multi-temporal LiDAR datasets. The person should show a passion for biodiversity and ecosystems and a deep understanding of how biodiversity and ecosystems change due to natural processes or human impact. Excellent programming skills in Python and R and experience in GIS and geospatial analyses are required. Additional experience in handling other Earth observation and remote sensing data is advantageous. The candidate should also show strong writing and verbal communication skills and have a successful track-record of publications.
You will develop scientific workflows in the context of establishing a Virtual Lab for ecological applications of LiDAR data. This will include the processing of various national, multi-terabyte airborne LiDAR data using a newly developed point cloud processing software tool Laserchicken, and a related High Performance Computing (HPC) processing pipeline called Laserfarm, both of which are implemented in Python. You will also process LiDAR data from the Global Ecosystem Dynamics Investigation (GEDI) using e.g. rGEDI. Other open source software tools for processing and visualization of point clouds and raster data (e.g. rLiDAR, raster, PDAL, GDAL) are also relevant. Using the derived metrics of ecosystem height, ecosystem cover, and ecosystem structural complexity you will develop ecological applications of LiDAR data with relevance to biodiversity and ecosystem science.
This will include national to global analyses of species distributions, ecosystem structure change, and/or mapping and classification of animal habitats using LiDAR and machine learning.