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Bayi Glacier in Qilian Mountain, China (Credit: Xiaoming Wang, distributed via imaggeo.egu.eu)

Job advertisement Open PhD project: Leveraging machine learning to refine leaf phenology representation in land surface models

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Open PhD project: Leveraging machine learning to refine leaf phenology representation in land surface models

Position
Open PhD project: Leveraging machine learning to refine leaf phenology representation in land surface models

Employer
International Max Planck Research School for Global Biogeochemical Cycles logo

International Max Planck Research School for Global Biogeochemical Cycles

In cooperation with Friedrich Schiller University Jena (FSU), the Max Planck Institute for Biogeochemistry (MPI-BGC) houses a unique and flexible research program that grants German and foreign students a broad selection of learning opportunities while still maintaining a research focus. The International Max Planck Research School for Global Biogeochemical Cycles (IMPRS-gBGC) offers a PhD program specializing in global biogeochemistry and related Earth system sciences.

Homepage: https://www.bgc-jena.mpg.de/en/imprs


Location
Jena, Germany

Sector
Academic

Relevant division
Biogeosciences (BG)

Type
Contract

Level
Entry level

Salary
We offer scholarships for 4 years and full-time contracts for 3 years within an international and multidisciplinary working environment. The starting date is flexible.

Preferred education
Master

Application deadline
5 August 2025

Posted
3 July 2025

Job description

Project description
Climate change is altering environmental conditions and directly impacting the carbon update and storage of terrestrial ecosystems. To understand these complex interactions, Terrestrial Biosphere Models (TBMs) such as QUINCY can be used to improve our understanding of the current and future feedbacks of climate change.

One key process that remains challenging for Terrestrial Biosphere Models (TBMs) to accurately simulate is leaf phenology, which strongly influences photosynthesis and overall plant growth. Leaf phenology encompasses the seasonal cycle of foliage expansion and senescence, both of which are key processes that can be critically influenced by environmental stressors like drought and are projected to be altered under future climate change scenarios. The impact of phenology extends beyond the simple effect of carbon dynamics, as it influences water and energy partitioning at the land surface.

The aim of this PhD thesis is to develop a data-driven algorithm to represent observed trends in phenology for different vegetation types into the QUINCY TBM, and evaluate the effect on land carbon, water and energy fluxes under current and future climates. The project will use machine-learning approaches to identify climate and ecosystem drivers of phenology based on existing leaf/ecosystem phenology data sets, e.g. at site-level via phenocams, or large-scale estimates from multiple satellite products such as MODIS or SENTINEL-2. Specifically the research programme will include the following steps:

Research program

  • Analysing and combining phenology data from different sources, e.g. phenocams; FLUXNET in-situ observations, and remote-sensing products, e.g MODIS and SENTINEL-2
  • Developing and training machine-learning based algorithms that predict leaf phenology across species and scales (site level to global)
  • Coupling the developed algorithm to the QUINCY biosphere models
  • Applying the enhanced QUINCY model at regional and global scales to assess patterns in phenology under current climate and future climate projections

Working group & collaborations
The PhD candidate will join the Terrestrial Biosphere modelling (TBM) at the Department of Biogeochemical Signals. The TBM group consists of skilled process based modelers and (amongst other things) is developing the ecosystem model QUINCY. This position is associated with the Max Planck Centre for Earth and intensive collaboration with experts from the center is expected.

Requirements for the PhD project are
Applications to the IMPRS-gBGC are open to well-motivated and highly-qualified students from all countries. Prerequisites for this PhD project are:

  • Master’s degree in environmental science, Earth system science, physics, mathematics, or related disciplines
  • Background in biogeoscience, ecosystem modelling, machine learning and/or remote sensing
  • Statistical skills or programming skills (e.g. Python, R, Mathematica)
  • Experience with “higher” scientific languages such as Fortran or C++
  • Excellent written and communication skills in English

The Max Planck Society (MPS) strives for gender equality and diversity. The MPS seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply. The MPS is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals.


How to apply

Application deadline for the fully funded PhD positions is August 5th, 2025. Pre-interviews via web conference will be carried out and promising candidates will be invited to take part in our selection symposium (September 30th – October 1st, 2025).

Find out more and apply online: https://www.bgc-jena.mpg.de/en/imprs/career-application

The Max Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals. The Max Planck Society seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply.