The Swiss Data Science Center (SDSC) is a national research infrastructure in data science and artificial intelligence (AI) of the ETH domain, with EPFL and ETH Zurich as founding partners. Its mission is to support academic labs, hospitals, industry and public sector stakeholders, including cantonal and federal administrations, through their entire data science journey, from the collection and management of data to machine learning, AI, and industrialization. With a large multidisciplinary team of professionals across three locations (Lausanne, Zurich, Villigen), the SDSC provides expertise and services to various domains, such as health and biomedical sciences, energy and sustainability, climate and environment, and large-scale scientific infrastructures.
The Swiss Data Science Center (SDSC) and the ETH Zurich’s Crop Science Group are seeking a Postdoctoral Researcher for the PhenoMix project, a Swiss National Science Foundation (SNSF) funded initiative.
This role sits at the intersection of machine learning, computer vision, agricultural sciences, and plant phenotyping. The position focuses on Automated Trait Estimation using Machine Learning, developing novel data science methods for crop mixture phenotyping.
The position will be based at the SDSC Zurich office (Andreasturm), with close collaboration with the Crop Science Group (Prof. Walter), the Grassland Sciences Group (Prof. Buchmann) at ETH Zurich’s Department of Environmental Systems Science (D-USYS), and with AGROSCOPE (Dr. Vogelgsang).
Context:
The PhenoMix project addresses the critical challenge of automated phenotyping for crop mixtures — a promising agricultural practice with significant potential for sustainable food production. The project leverages the Field Imaging Platform (FIP), a state-of-the-art high-throughput phenotyping facility, along with field experiments to generate unprecedented multi-modal datasets of pure stands and crop mixtures. The project will also contribute to the creation of new generation phenotyping datasets – including 3D reconstructions and derived trait information – and related models, which will be made publicly available
The postdoctoral researcher will create novel data science tools and automate processing of image time series, plant trait information, and 3D reconstructions. The work will bridge advanced machine learning methods with practical agricultural applications, developing models that can transfer knowledge across different imaging platforms and environmental conditions. The postdoc will be responsible for delivering advances and solutions that not only advance the state-of-the-art, but also have real-world impact for farmers, breeders, and researchers in the field of plant phenotyping.
Collaboration:
The postdoctoral researcher will be part of a highly collaborative and interdisciplinary project, working closely with experts in machine learning, plant phenotyping, crop sciences, and field validation. The project is designed to foster knowledge exchange and collaboration across disciplines, ensuring that the developed methods are both scientifically rigorous and practically relevant.
This project brings together expertise from multiple leading groups. The SDSC provides expertise in machine learning, computer vision, and data science infrastructure, serving as the primary host institution for this position. The Crop Science Group (Prof. Achim Walter, ETH Zurich) operates the Field Imaging Platform (FIP) and and brings deep expertise in high-throughput plant phenotyping and crop science, providing access to cutting-edge infrastructure and datasets. The Grassland Sciences Group (Prof. Nina Buchmann, ETH Zurich) contributes key expertise in plant ecophysiology, biodiversity and plant ecology. The Extension Arable Group (Dr. Susanne Vogelgsang, AGROSCOPE) provides key expertise in variety testing and agronomic suitability, as well as plant pathology. The postdoc will collaborate and exchange with all partners, depending on project requirements.
The postdoc will develop and implement cutting-edge machine learning approaches for automated trait estimation, focusing on:
- Foundation Models for Phenotyping: Leveraging and adapting pre-trained foundation models for crop trait estimation in both pure stands and crop mixtures, minimising computational and data annotation overheads while maximising generalisation power
- Domain Transfer Methods: Developing plant-aware image-based domain transfer techniques to enable models trained on high-resolution FIP images to work effectively with lean device images (e.g., smartphone cameras)
- 3D Reconstruction and Rendering: Creating 3D point clouds from multi-view setups and rendering realistic 2D images across different viewpoints, leveraging among many approaches generative models, neural rendering and implicit models
- Human-in-the-Loop Approaches: Implementing active learning strategies that incorporate expert feedback at inference time, enabling real-time model correction and improvement with minimal labelling budget
- Field Evaluation: Conducting rigorous qualitative and quantitative evaluations of developed models on farm field experiments, integrating expert feedback to improve model performance
- Data Product Generation: Preparing comprehensive time series datasets of derived products, including raw data, 3D reconstructions, model estimations, and reference measurements for downstream analyses
- Software Development: Developing and maintaining codebases for the implemented methods, ensuring reproducibility, and facilitating future research and applications in the field of plant phenotyping
Research and Development
- Design, develop and implement foundation model-based approaches for multi-trait plant phenotyping
- Extend and implement domain-specific and plant-specific, physiologically plausile, machine learning models
- Develop and evaluate domain transfer and adaptation methods for cross-platform phenotyping
- Design and deploy human-in-the-loop and active learning strategies
- Conduct field experiments and evaluate model performance in real-world field conditions
- Engage with diverse stakeholders including researchers, farmers, and breeders
Collaboration and Scientific Communication
- Process and help curating large-scale multi-modal datasets from the FIP and field experiments
- Supervise and collaborate with students at different levels providing guidance and supervision
- Contribute to existing codebases and engage with open source communities
- Prepare publications for top-tier scientific venues
- Present research findings at conferences, seminars and workshops
- Communicate complex technical concepts to both expert and general audiences
Education:
- PhD in relevant field such as computer science, machine learning, data science, or domain science (e.g., plant phenotyping, agricultural sciences, environmental sciences) with demonstrated expertise in machine learning and computer vision
- Demonstrated research excellence through publications in relevant venues
Technical and Research Expertise:
- Strong background in machine learning and deep learning, particularly computer vision, with hands on experience in foundation models, transfer learning, domain adaptation
- Solid experience with modern deep learning frameworks (PyTorch preferred)
- Proven ability in scientific programming and prototyping in Python
- Ability to formulate research questions and design experiments independently
- Experience handling large and complex multi-modal datasets
Soft Skills:
- Excellent communication skills in English (written and oral)
- Positive attitude towards interdisciplinary collaboration
- Ability to work independently while contributing to team objectives
Other beneficial/relevant competencies:
- Experience with 3D reconstruction techniques (structure from motion, neural rendering, etc.)
- Knowledge of active learning, human-in-the-loop, Bayesian optimisation
- Familiarity with agricultural sciences, plant phenotyping, or related domains
- Experience implementing, training and evaluating models for spatio-temporal data
- Interest in sustainable agriculture, crop science, or food safety challenges
Professional Development:
- A stimulating, collaborative, diverse and cross-disciplinary research environment
- Opportunity to work with state-of-the-art phenotyping infrastructure and datasets
- Access to computational resources and latest machine learning tools
- Possibility to publish research in top-ranked conferences and journals
- Opportunity to travel and present work at international events
- Involvement in supervision of MSc and BSc students
- Participation in lectures and teaching activities
Work Environment:
- Position hosted at the Swiss Data Science Center with offices at ETH Zurich and EPFL
- Collaborative environment spanning multiple institutions and research groups, within PhenoMix and beyond
- We value work-life balance
- Beautiful locations in Zurich with excellent quality of life
Starting Date and Duration:
- Starting date: August or by mutual agreement
- Duration: Up to 4 years (SNSF project funding duration)
In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.
We look forward to receiving your online application with the following documents:
- Letter of Motivation (max 2 pages) explaining your interest in the position and relevant experience
- Curriculum Vitae including publication list
- Electronic copies of relevant academic diplomas, transcripts and certificates
- Contact details from 2 to 3 references
- Links to code repositories or portfolios (if available)
Further information about Swiss Data Science Center can be found on our Website. Questions regarding the position should be directed to Dr. Michele Volpi, [email protected] (no applications).
Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.
