Novi Sad, Serbia
Finished post-doc, good publication track record, innovation-driven mentality, capable of working individually as well as a part of the team. Excellent English.
Understanding of challenges in the global agrifood sector is desirable.
Exceptionally, excellent candidates who have not finished post-doc will be considered. (PhD is required.)
Expertise and a long-standing experience in one of the following fields (or similar): remote sensing, satellite image processing, multi-dimensional image processing (multispectral, hyperspectral), data fusion, machine learning. Experience in: Phyton, GDAL, Matlab, IDL, QGIS, Google EE, and/or ENVI. Strong ability for interdisciplinary research. Experience in satellite imaging in agriculture, UAV-based imaging, design and validation of biophysical variables and vegetation indices derived from satellite and airborne multispectral/hyperspectral instruments as part of their calibration and quality assessment process would be an asset but is not required.
Use of optical and SAR satellite images to better understand and model various processes in agriculture (plant, soil, water), hydrography, forestry, environment, etc. Satellite-image-based crop structure detection, yields estimation. Detection of land-use and land-change. Development of novel data-based products for use in agriculture. Modelling of energy exchange between plant and environment. Design and development of user oriented scalable and elastic system targeting different application scenarios in agriculture and environment monitoring.