PhD Position on Mapping biodiversity using data driven methods in combination with citizen sci[...]

Job description

PhD Position on Mapping biodiversity using data driven methods in combination with citizen science audio recordings and satellite imagery

We are seeking a PhD student to investigate data-driven biodiversity classification in a stakeholder process, particularly examining how audio recordings collected by citizen scientists and satellite imagery can be used to map biodiversity cost-effectively at a large scale.

The overarching goal is to develop novel machine-learning approaches that integrate acoustic and remote-sensing data for biodiversity monitoring, culminating in practical applications for environmental authorities across Germany.

You will be joining (together with two Postdocs) the project Bio-O-Ton-2, i.e., BiodivKI-2: Biodiversity Assessment of Habitat Types Using Machine Learning Based on Citizen Science Audio Recordings and Satellite Imagery, funded through the BMBF.

The Project :

Bio-O-Ton-2 aims to revolutionize biodiversity monitoring by capitalizing on soundscapes (e.g., bird calls, insect noises) collected by citizens nationwide, and combining these recordings with satellite-based remote-sensing data.

The methodology will be refined through close collaboration with stakeholders—such as nature conservation authorities and NGOs—to ensure the resulting classification tools address real-world needs.

Core challenges include validating large, heterogeneous datasets, integrating acoustic and geospatial inputs, and building an interactive platform that visualizes site-specific biodiversity metrics for end users.

Your Tasks :

• Data Management: Gather, clean, and manage a large, diverse collection of audio (from Citizen Science) and satellite (e.g., Sentinel-2, PlanetScope) datasets.
  
  
• Data Pipelines: Develop Python-based workflows to standardize, merge, and prepare acoustic and remote-sensing data for machine-learning analyses.
  
  
• Platform Development: Create an intuitive, interactive visualization platform (e.g., Python/Shiny) for classification results, tailored to stakeholder feedback.
  
  
• AI Collaboration: Collaborate with AI experts to design, train, and validate deep-learning models for biodiversity classification.
  
  
• Field Campaigns: Participate in targeted field campaigns to record additional sound data, ensuring a robust training/validation dataset.
  
  
• Stakeholder Engagement: Contribute to working with conservation authorities, NGOs, and scientific partners in a co-creation process to align model outputs with practical, real-world biodiversity-monitoring requirements.
  
  
• Transdisciplinary research: Matching expert knowledge from different fields with practical experts in biodiversity mapping.
  
  
• Communication & Dissemination: Present findings at academic conferences and workshops; co-author publications in peer-reviewed journals.
  
  

You must hold a master’s degree in a discipline of relevance (e.g., Geoinformatics, Geoecology, Computer Science, Biology, Biophysics, Environmental Science, Spatial Planning or others) and be willing to relocate to Karlsruhe, Germany.

German language skills are beneficial, English language skills are expected.

Ideally, you are a creative, yet analytically thinking individual with a thirst for knowledge who values a diverse workplace.

Beneficial experience and qualities include:

• Proven (or strong interest in) geospatial data handling, machine learning, and/or acoustic data analysis.
  
  
• Proficiency in Python (or willingness to acquire) and familiarity with data-science libraries.
  
  
• (Willingness to acquire) skills in python’s Shiny package to create interactive maps.
  
  
• Motivation to tackle real-world biodiversity challenges with a creative and analytical mindset, and readiness to work in an interdisciplinary, collaborative setting.
  
  

What we offer:

We offer a 3-year contract.

During your PhD, you will be paid 75% of the Collective Agreement for the German Public Service Sector (TV-L EG13).

We anticipate an extension for a 4th year (funding permitting).

You will join the PhD program of the KIT-Department of Civil Engineering, Geo and Environmental Sciences where you have the option to complete your PhD as a cumulative thesis.

You are also encouraged to participate in GRACE, the Graduate School for PhD students of the KIT-Center Climate and Environment.

It is the goal of GRACE to provide to its students not only highly specialized and interdisciplinary knowledge but also important key skill qualifications.

GRACE also offers scholarships to PhD students for a 3-month long research stay abroad.

Furthermore, you will have access to a wide range of courses for professional development offered through the Karlsruhe House of Young Scientist (KHYS).

The University:

You will be part of the junior research group GRUSS at the Institute of Photogrammetry and Remote Sensing (IPF) located at the main campus of the Karlsruhe Institute of Technology (Campus Süd), right next to the 300-year-old Karlsruhe Palace.

IPF combines competence in computer vision, remote sensing, geoinformatics, and active sensing and is therefore the perfect home for an interdisciplinary project using methods of large-scale geospatial data analysis.

You will be a member of a growing, multi‐disciplinary, highly collaborative, and young team, well connected to national and international research networks and activities.

We offer you an attractive and modern workplace with access to the excellent equipment of the KIT, a varied work, a wide range of training opportunities, flexible working time models, an allowance for the job ticket Germany and a casino/cafeteria.

How to apply:

If you are interested, please send your application including a cover letter, your CV, and all certificates/diplomas in electronic form to Dr. Susanne Benz by Feb 18, 2025 .

You may also contact her with any further questions.

Required Skill Profession

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