PhD Candidate / Research Assistant at ETH Zurich

A new PhD position in environmental sciences is available at ETH Zurich. There is no application deadline for this position.

ETH Zurich is one of the world's leading universities specialising in science and technology. It is renowned for its excellent education, its cutting-edge fundamental research and its efforts to put new knowledge and innovations directly into practice. The EcoVision Lab, led by Dr. Jan Dirk Wegner and part of the Photogrammetry and Remote Sensing group (Prof. Konrad Schindler), does research at the frontier of machine learning, computer vision and remote sensing to solve questions in the environmental sciences. Research centers on innovative (deep) machine learning, big data analysis technology and their combination with forward-modelling approaches. The EcoVision Lab regularly publishes at top computer vision conferences like CVPR as well as at remote sensing and environmental sciences events. We offer an exciting and stimulating environment to study and work in: ETH has several internationally recognized research groups dedicated to computer vision and image metrology, and we also collaborate with several other institutions and companies in the fields of computer vision, machine learning, 3D metrology and earth observation, in Switzerland and abroad. new position.We are looking for candidates with an interest in performing cutting edge research, strong motivation, and a desire to learn. An ideal candidate will have an excellent degree (M.Sc., M.Eng. or equivalent) in Computer Science, Robotics, Geodesy/Geomatics, or a related field (e.g. Electrical Engineering, Applied Mathematics), strong mathematical understanding, and programming experience, preferably in C++ and/or Python.

The successful candidate will be fully funded and work on a project in cooperation with an industrial partner, aiming to develop a deep learning approach to predict yield of crops in the tropics. We will explore exciting research directions like bayesian deep learning, semi-supervised learning, domain shift, and generative adversarial networks.

Prior experience in machine learning, computer vision and remote sensing is a plus, fluency in English is required (both written and spoken).


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