Doctoral candidate (PhD) (65%; d/f/m)

06.02.2024, Wissenschaftliches Personal

The research initiative “A-DUR” intends to develop interdisciplinary and transdisciplinary solutions to the current challenges faced by degraded floodplain forests near urban areas in Germany. Focusing on the "Floodplain Forests along the Middle Isar," (Naturwald Mittlere Isar) the project examines the impacts of altered site conditions, forestry practices, and recreational activities. It aims to derive recommendations for the revitalization and regeneration of these forests and identify their multifunctional potential for biodiversity, local recreation, and climate protection. This endeavor is collaborative, involving a mix of academic, governmental, and private sector partners, offering opportunities to PhD candidates to gain insights into various field of science and practice.
“A-DUR” is comprised of 5 social science and natural science sub-projects, next to governmental and non-governmental organizations. All stakeholders will work closely together.

What we offer:

• An innovative and lively working environment at the university and campus
• Access to modern facilities and infrastructure at a strong research department
• Scientific exchange, flexibility, independence and self-responsibility
• Extensive options of vocational training (meetings, workshops, conferences)
• A chance to receive your doctoral degree
• TV-L E13 (65%), initially limited to 3 years

Topic:
Forest Dynamics and Carbon Sequestration
This subproject aims to deepen our understanding of forest dynamics and their role in carbon sequestration within the Naturwald Mittlere Isar region. It encompasses detailed analysis of forest structure variations due to historical land use and species composition, including coniferous forests, hybrid poplar stands, and natural forest types. The project leverages existing inventory data and cutting-edge Mobile Laser Scanning (MLS) technology to upscale findings through Airborne Laser Scanning (ALS) data. It also focuses on estimating above-ground carbon storage, especially in hardwood floodplain forests, and ongoing carbon sequestration in relation to forest dynamics such as growth, mortality, and regeneration.

Job description:
The successful candidate will be actively involved in terrestrial acquisition of forest structure for the assessment of forest dynamics and carbon binding, utilizing the handheld laser scanner ZEB Horizon for detailed and precise 3D mapping of forest plots. This role includes scanning of designated plots to digitize forest structures, followed by computer-assisted classification of point clouds to identify individual trees and derive conventional and novel metrics for forest inventory. The candidate will also contribute to airborne upscaling of forest structure from selected plots using drone technology in collaboration with the practice partner SCIMOND, and work on estimating above-ground carbon storage and sequestration through Quantitative Structural Models (QSM) of single trees from point cloud data. Additionally, the role involves conducting light measurements using the Solariscope SOL300 to analyze light conditions and their impact on forest regeneration and dynamics, with a focus on deriving management recommendations based on differentiated forest types and development phases. The job will be associated with the TUM Professorship for Forest and Agroforest Systems in Freising, DE, but will be jointly supervised together with Prof. Andreas Rothe from HSWT.

The candidate we are looking for ideally has:

• A strong passion for forest ecology and conservation, with a keen interest in understanding the complex interplay between forest dynamics and carbon sequestration processes
• A solid background in remote sensing and its application to forest ecology
• Experience with core sampling and retrospective analysis for carbon sequestration studies, and familiarity with software for analyzing tree rings
• Innovative thinking and problem-solving abilities to adapt methodologies and derive novel insights from complex data sets
• A commitment to contributing to sustainable forest management practices through research and the dissemination of findings to both the scientific community and practice partners

Job requirements:

• Advanced degree in forestry, environmental science, ecology, or a related field
• Strong analytical skills, with experience in forest inventory metrics, carbon estimation methodologies, and statistical analysis, including predictive modeling techniques
• Proficiency in software tools relevant to the analysis of point clouds and forest inventory data (e.g., Lidar360) or a willingness to learn and utilize new tools as required
• Ability to conduct fieldwork in forested environments, including operating handheld laser scanning equipment and participating in drone-based aerial surveys
• Excellent communication and collaboration skills, with the ability to work effectively as part of a multidisciplinary team and under the guidance of project leads
• experience with laser scanning technology, particularly terrestrial and airborne laser scanning (TLS and ALS), and proficiency in handling and interpreting point cloud data is an advantage
• Driving license level B
• German Level Skill B2

Starting date:
A.S.A.P. (part-time 65%, fixed-term for 3 years)

Interested?
Please send your application with: (1) a 1-page letter of interest including a short outline of career goals and research experience; (2) a detailed CV; and (3) contact information of two referees. Please send these documents in the form of one single pdf-file (ADUR_TP02_surname_forename_appldoc.pdf)
by Friday, 16.02.2024 to: Prof. Dr. Peter Annighöfer; [email protected]

Questions regarding project or position?
Please contact: Prof. Annighöfer and/or Prof. Rothe or visit our webpages for more information on our research groups and the kind of work we do.

Application closing date:
Friday, 16.02.2024

The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.

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Kontakt: [email protected]