Postdoc in deep learning based remote sensing data analysis

This postdoctoral position focuses on advanced research in deep learning techniques applied to remote sensing data analysis. The role requires expertise in machine learning, particularly deep learning frameworks such as TensorFlow or PyTorch, to develop and implement algorithms for processing and interpreting large-scale remote sensing datasets. Candidates should have a strong background in computer vision, image processing, and data analytics, with experience handling multispectral, hyperspectral, or radar satellite imagery. Proficiency in programming languages like Python and familiarity with geospatial data formats and tools such as GDAL or QGIS are essential. The position involves designing novel models to extract meaningful insights from complex environmental or geospatial data, requiring strong analytical skills and a solid understanding of remote sensing principles. Experience with cloud computing platforms or high-performance computing environments to manage and process big data is advantageous. The ideal candidate will demonstrate a track record of scientific research, including publications in relevant journals, and the ability to work independently as well as collaboratively in interdisciplinary teams. Strong communication skills are necessary to present findings and contribute to academic and applied research projects. This role is suited for candidates with a doctoral degree in computer science, electrical engineering, geoinformatics, or a related field, with a focus on deep learning and remote sensing applications.