PhD Position - Drone-Based Remote Sensing and Agroecosystem Modeling

In the coming decades, climate change and land use change will have a significant impact on the performance of terrestrial ecosystems in terms of their services (food, feed, fiber, energy) and the challenges facing society. At the Institute of Bio- and Geosciences ­- Agrosphere (IBG‑3), we are developing solutions to secure ecosystem services based on an improved understanding of hydrological and biogeochemical processes in terrestrial systems. The focus is on agricultural and forestry systems, which are coming under increasing pressure as a result of global change. Innovative observation technologies are combined with laboratory experiments and modeling to research and predict terrestrial processes across scales (from the pore scale to the field scale to the continental scale), thereby contributing to the sustainable use of natural resources such as water, soil, and the atmosphere.

Project Overview

The PhD position is offered within the Cluster of Excellence PhenoRob – Robotics and Phenotyping for Sustainable Crop Production. We are looking for a highly motivated PhD candidate to join our world‑leading research program in robotics, remote sensing, and data‑driven approaches for sustainable agriculture. PhenoRob’s mission is to transform crop production by optimizing breeding and farm management through innovative sensing technologies, advanced modeling, and intelligent automation. This PhD project contributes to PhenoRob’s initiative to develop digital twins for agroecosystems, i.e., high‑fidelity virtual representations of agricultural ecosystems. These digital twins will enhance our understanding of ecosystem functioning, more specifically water and carbon fluxes, supporting the design of more sustainable and resilient crop production systems. The project will focus on integrating high‑resolution drone‑based remote sensing data, including multispectral and thermal imagery as well as LiDAR measurements, into ensemble agroecosystem model simulations. The successful candidate will play a key role in developing robust landscape‑scale digital twins and advancing data assimilation techniques for agricultural and environmental applications.

Responsibilities

• Fly drones and process data of multispectral, thermal and LiDAR sensors.
• Implement approaches to integrate drone data into an existing agroecosystem data assimilation framework.
• Use radiative transfer models to translate spectral data into model state variables (e.g., LAI, canopy temperature) and convert them into synthetic observations.
• Evaluate the performance of the data assimilation framework in controlled experiments and evaluate its potential for spaceborne remote sensing and larger landscape‑scale applications.
• Improve simulation accuracy for key ecosystem variables such as gross primary productivity (GPP) and net ecosystem exchange (NEE).
• Work with data from PhenoRob experimental field sites, equipped with Eddy Covariance towers, to generate digital twins and quantify model uncertainties.

Qualifications

• Master’s degree in geography, remote sensing, geoscience, physics, computational geoscience, or related natural sciences with an overall grade of at least good.
• Experience in programming (e.g., Python, Fortran).
• Advanced knowledge of numerical methods.
• Experience with flying drones and drone data processing (thermal IR, multispectral, LiDAR), fieldwork experience.
• Strong English communication skills. Knowledge of the German language is considered an asset.
• Good communication and organizational skills are essential for interdisciplinary cooperation with several researchers and technicians.
• Driver’s licence and EU drone licence is required (or should be obtained within the first 3 months).
• Fieldwork is required for manual soil and plant measurements and transport of equipment to the field.
• Very good command of written and spoken English (at least B2 level according to the CEFR ...).

Benefits

• Working in an interdisciplinary environment as well as excellent facilities for remote sensing research and numerical simulation and inversion.
• Opportunities to be a part of the national and international scientific community.
• PhD students are encouraged to attend international conferences and a three‑month research stay abroad with a cooperating partner is possible.
• Working in the heart of Europe.
• Partial remote (home office) work may be arranged/disscused with main supervisor approval, outside the growing season once fieldwork is completed.
• Dynamic and diverse working group and work environment.
• Your professional development is important to us – we support you specifically and individually e.g., through training and networking opportunities specifically for doctoral candidates (JuDocS):
• 30 days of annual leave.
• Targeted services for international employees, e.g. through our International Advisory Service.

The position is for a fixed term and planned to run for 3 years with a potential extension of 1 year. Pay is in line with 70% of pay group 13 of the Collective Agreement for the Public Service (TVöD‑Bund) and additionally a special payment ("Christmas bonus"). All information about the TVöD‑Bund collective agreement can be found on the BMI website (pay scale table on page 69 and following of the PDF download): Further information on doctoral degrees at Forschungszentrum Jülich (including its various branch offices) is available at In addition to exciting tasks and a collegial working environment, we offer you much more:

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realise their potential is important to us. The following links provide further information on diversity and equal opportunities: and on the targeted promotion of women:

We look forward to receiving your application. The job will be advertised until the position has been successfully filled. You should therefore submit your application as soon as possible.