Postdoc grassland modeller (f/m/d)
Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF) e.V.
The mission of the Leibniz Centre for Agricultural Landscape Research (ZALF) as a nationally and internationally active research institute is to deliver solutions for an ecologically, economically and socially sustainable agriculture – together with society.
ZALF is a member of the Leibniz Association and is located in Müncheberg (approx. 35 minutes by regional train from Berlin-Lichtenberg). It also maintains a research station with further locations in Dedelow and Paulinenaue.
The KIKompAg project addresses important challenges in agricultural research, such as monitoring agroecosystems at different scales, by integrating artificial intelligence (AI) into the current agricultural toolbox. The main objective of KIKompAg is to develop a coherent approach for integrating multimodal data, AI and simulation methods to characterise agricultural systems across scales and, building on this, to create a comprehensive curriculum covering multiple aspects of agroecosystem analysis with data from multiple sources. The framework combines state-of-the-art remote and close sensing products with various deep learning and mechanistic models, as well as diverse surface and subsurface reference datasets for both cropland and grassland.
Subject to funding, we are offering a full time position temporarily limited until 30 September 2025 at our location in Müncheberg as
Postdoc grassland modeller (f/m/d)
- derivation of a high-resolution groundwater-floodplain distance map from tide gauge data and a digital terrain map
- separation of grassland vegetation classes and determination of biomass growth patterns using remote sensing data and Unsupervised Learning
- use multivariate statistics to determine the individual and combined contributions of biotope type and groundwater-floodplain distance to biomass growth
- predict biomass growth and forage yield by fusing a mechanistic plant growth model with a mechanistic species competition model using conditional inference tree algorithms
- development of learning materials for knowledge transfer
- academic background in the field of field of Agriculture (preferably grassland), Environmental science, Geosciences or related fields
- programming experience in Python or extensive experience in another high-level programming language is a prerequisite
- experience with mechanistic simulation models and/or data-driven modelling approaches
- ability to work independently as a scientist (proven by completed doctorate/PhD or equivalent publication successes)
What we offer:
- an interdisciplinary working environment that encourages independence and self-reliance
- classification according to the collective agreement of the federal states (TV-L) up to EG 13 (including special annual payment)
- access to modern workspace with modern IT infrastructure and HPC
- a collegial and open-minded working atmosphere in a dynamic research institution
Women are particularly encouraged to apply. Applications from severely disabled persons with equal qualifications are favored. It is generally possible to work in the position on a part-time basis. Please send your application preferably online (see button online application below). For e-mail applications, create a PDF document (one PDF file, max. 5 MB; packed PDF documents, archive files like zip, rar etc. Word documents cannot be processed and therefore cannot be considered!) with the usual documents, in particular CV, proof of qualification and certificates,
- reference number 96-2022 until 12 October 2022
to (see button e-mail application below).
- If you have any questions, please do not hesitate to contact us: Dr. Gohar Ghazaryan, Tel. +49 (0) 33432/82-411, Email: firstname.lastname@example.org or Prof. Dr. Masahiro Ryo, Tel. +49 (0) 33432/82-206, Email: email@example.com.
Application documents sent by post or extensive publications will not be returned
For cost reasons, application documents or extensive publications can only be returned if an adequately stamped envelope is attached.
If you apply, we collect and process your personal data in accordance with Articles 5 and 6 of the EU GDPR only for the processing of your application and for purposes that result from possible future employment with the ZALF. Your data will be deleted after six months.
- Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF) e.V.
Eberswalder Straße 84
- Dr. Gohar Ghazarya
- +49 (0) 33432/82-411
- (Ursprünglich) veröffentlicht am: