Leibniz Centre for Agricultural Landscape Research
1 week ago
Postdoctoral Researcher in AI, Digital Phenotyping and Plant Genetics for Climate-Resilient Agriculture Lithuanian Research Centre for Agriculture and Forestry in Germany
Degree Level
Postdoc
Field of study
Computer Science
Funding
Approximately €2,600 per month after taxes, with opportunities for additional income through funded projects. The position is offered for two years with a possibility of extension.
Country
Germany
University
Leibniz Centre for Agricultural Landscape Research

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About this position
We’re hiring a Postdoctoral Researcher at the Lithuanian Research Centre for Agriculture and Forestry (LAMMC), Laboratory of Genetics and Physiology, to work at the intersection of AI, digital phenotyping, plant genetics, and climate-resilient agriculture.
The project focuses on developing methods to analyse UAV and ground-based RGB, multispectral, hyperspectral, and 3D imagery, while integrating genomic, transcriptomic, and agronomic data to better understand crop growth, stress responses, and yield potential.
Ideal candidates should hold a relevant PhD (awarded within the past seven years) and bring strong Python/R and statistical or machine learning skills. Experience with genomic/transcriptomic datasets and spectral sensing or high-throughput phenotyping is specifically highlighted.
The position is based in Akademija, Kėdainiai district, Lithuania. Funding is approximately €2,600 per month after taxes, with opportunities for additional income through funded projects. The appointment lasts two years with a possible extension.
To apply, send a CV, a brief motivation letter, and the contact details of two referees to [email protected].
Funding details
Approximately €2,600 per month after taxes, with opportunities for additional income through funded projects. The position is offered for two years with a possibility of extension.
What's required
Applicants should have a relevant PhD, with the PhD awarded within the past seven years. Strong Python and/or R skills and statistical or machine learning experience are required. Experience with genomic and transcriptomic data and spectral sensing or high-throughput phenotyping is expected.
How to apply
Send your CV, a brief motivation letter, and contact details for two referees to the listed email address.
More information can be found here
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