The Hydrology, Agriculture and Land Observation (HALO) group at KAUST is seeking high-quality candidates for a number of Postdoctoral positions in areas related to precision agriculture, agricultural informatics, plant-phenotyping, big-data analytics, and machine learning. These positions may include (but are not limited to):

• high-resolution terrestrial remote sensing (Sentinel-2, Planet CubeSats, other satellite platforms)
• hyperspectral analysis and interpretation of agricultural systems (UAV-, field-, or laboratory-based)
• large scale (nation-to-global) mapping and analysis for developing near real-time informatics (e.g. crop-type, planting and harvest dynamics, irrigation scheduling, crop condition, water-use, yield potential etc.) using Google Earth Engine or similar platforms
• multiple areas of UAV-related research (3D reconstruction, vicarious calibration, multi-sensor fusion, etc.) - ideally with experience in thermal, lidar, multi-spectral or hyper-spectral sensing

We are also working with collaborators on projects exploring the application of novel field-based systems (cosmic ray neutron sensor and GNSS) and satellite (GNSS-R) in agriculture, as well as emerging carbon accounting approaches (combining high-resolution remote sensing, in-situ sensing, and modeling) of terrestrial landscapes, with a focus on agricultural, desert and mangrove systems (in the context of nature-based solutions to climate change). 

The HALO research group consists of approximately 15 researchers and includes a well-resourced laboratory, supporting infrastructure and state-of-the-art equipment, as well as access to a range of high-performance computing systems. In addition to a generous tax-free salary, benefits such as accommodation, vacation allowance, medical insurance and relocation allowance will also be offered. PhD scholarships cover the full cost of tuition and an associated stipend. 

• Applicants will hold a PhD in a related field, with demonstrated experience (e.g. publications) in one or more of the open positions
• Applicants with prior experience in machine learning, operation of UAVs, big-data analytics are particularly encouraged
• Previous experience in the field - particularly in the installation and commissioning of advanced scientific equipment will be viewed favorably

To express your interest in any of these positions, please contact Prof Matthew McCabe (matthew.mccabe@kaust.edu.sa) or Dr Kasper Johansen (kasper.johansen@kaust.edu.sa), including a copy of your CV and an expression of interest in one (or more) of the positions. Please include an explanation of your relevant experience and previous publications in these areas, along with the names of at least two potential referees (referees will not be contacted until after initial screening and interview of prospective candidates).