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Postdoctoral Position in Machine Learning for Automated Plant Phenotyping (PhenoMix Project) Zurich, Switzerland

Postdoc Position at University of Zurich Switzerland

Summary

The Swiss Data Science Center (SDSC), in collaboration with ETH Zurich’s Crop Science Group, seeks a Postdoctoral Researcher for the PhenoMix Project. This role focuses on developing automated trait estimation methods using machine learning, aimed at advancing crop mixture phenotyping for sustainable agriculture.

Postdoctoral Position in Machine Learning for Automated Plant Phenotyping (PhenoMix Project) Zurich, Switzerland

Designation

Postdoctoral Researcher – Machine Learning for Automated Plant Phenotyping

Table

DetailsInformation
Research AreaMachine Learning, Computer Vision, Agricultural Sciences
LocationZurich, Switzerland
Eligibility/QualificationPhD in relevant field (Computer Science, Machine Learning, etc.)
Job DurationUp to 4 years (SNSF project funding)
Starting DateAugust or by mutual agreement

Job Description

Responsibilities:

  • Develop and implement cutting-edge machine learning approaches for automated trait estimation.
  • Design foundation models tailored for phenotyping.
  • Extend domain-specific machine learning models for cross-platform phenotyping.
  • Conduct field experiments and rigorously evaluate model performance.
  • Collaborate with interdisciplinary teams and engage with diverse stakeholders.

Key Tasks:

  • Prepare and curate multi-modal datasets.
  • Develop software and maintain reproducible codebases.
  • Supervise students and contribute to scientific publications.
  • Present research at conferences and communicate findings to general audiences.

How to Apply

Interested candidates should submit the following documents:

  • Letter of Motivation (max 2 pages)
  • Curriculum Vitae including publication list
  • Electronic copies of relevant academic diplomas, transcripts, and certificates
  • Contact details for 2-3 references
  • Links to code repositories or portfolios (if available)

Applications must be submitted through the online application portal.

Last Date for Apply

Open Now

Link

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