Postdoctoral Position in Machine Learning for Automated Plant Phenotyping (PhenoMix Project) ETH Zürich in Switzerland

The Swiss Data Science Center (SDSC) at ETH Zurich, in collaboration with the Crop Science Group, Grassland Sciences Group, and AGROSCOPE, invites applications for a postdoctoral position in the PhenoMix project, funded by the Swiss National Science Foundation (SNSF). This interdisciplinary project focuses on developing advanced machine learning and computer vision methods for automated plant phenotyping, with a strong emphasis on sustainable agriculture and crop science.

The successful candidate will join a vibrant, multidisciplinary team at the SDSC Zurich office, working closely with leading experts including Prof. Achim Walter (Crop Science Group), Prof. Nina Buchmann (Grassland Sciences Group), and Dr. Susanne Vogelgsang (AGROSCOPE). The project leverages the Field Imaging Platform (FIP), a state-of-the-art high-throughput phenotyping facility, to generate and analyze multi-modal datasets from pure stands and crop mixtures. The postdoc will develop novel data science tools for automated processing of image time series, plant trait information, and 3D reconstructions, bridging advanced machine learning with practical agricultural applications.

Key responsibilities include designing and implementing foundation model-based approaches for multi-trait plant phenotyping, developing domain transfer and adaptation methods, creating 3D point clouds and neural renderings, and deploying human-in-the-loop and active learning strategies. The role also involves conducting field experiments, evaluating model performance, and generating comprehensive datasets for downstream analyses. The postdoc will contribute to open-source codebases, supervise students, and present research at top-tier conferences and workshops.

Applicants must have a PhD in computer science, machine learning, data science, or a related field, with demonstrated expertise in machine learning and computer vision. Strong programming skills in Python and experience with deep learning frameworks (preferably PyTorch) are essential. Additional desirable skills include experience with 3D reconstruction, active learning, and familiarity with agricultural sciences or plant phenotyping. Excellent communication skills and a collaborative mindset are required.

The position offers up to 4 years of SNSF funding, a stimulating and diverse research environment, access to cutting-edge infrastructure and computational resources, and opportunities for professional development and international collaboration. ETH Zurich values diversity, sustainability, and work-life balance, providing an inclusive and supportive environment for all staff and students.

To apply, submit your application via the ETH Zurich online portal, including a motivation letter, CV with publication list, academic transcripts, references, and code portfolio links if available. For further information, contact Dr. Michele Volpi ([email protected]). Applications via email or postal services will not be considered.