Job offer

With KIForst, we are establishing a faculty-wide infrastructure that brings together expertise in artificial intelligence and its applications in forest sciences. We are looking for a methodologically strong and communicative individual with an interest in the broad range of forest science research questions-someone who not only develops new solutions, but also works closely with researchers to put them into practice.

Your role at KIForst You will work at the interface of machine learning, deep learning, data science and applications in forest sciences. Together with the Director, you will further develop KIForst as a faculty-wide platform for methodological support, consulting and training. You will design solutions and independently implement key methodological and technical components, with a particular focus on model explainability and trustworthy AI, while also enabling researchers to apply AI methods in a sound and informed way.

Your responsibilities

• Translate research questions into appropriate data science and AI approaches and support selected projects from problem definition through to validated implementation.
• Develop solutions using machine learning, deep learning and computer vision, and independently implement key components in Python.
• Develop explainable AI approaches that enable forestry experts to understand and build confidence in model decisions.
• Support research groups in selecting methods, study design, data preparation, modelling and computing infrastructure, and contribute to gradually establishing an AI consulting service.
• Design and deliver teaching activities, workshops, professional training courses and reusable learning materials for different target groups.
• Contribute to third-party funding proposals and scientific publications.

Part of the working hours is set aside for academic development and can be used, in particular, to develop one’s own academic profile, produce one’s own publications and prepare one’s own applications for third-party funding, which ideally tie in with KIForst’s pilot projects and cross-cutting tasks. The members of the KIForst Steering Committee provide support in this regard as academic mentors., * An opportunity to play an active role in shaping and establishing a new faculty-wide competence center.

• Varied scientific work addressing a broad range of research questions in forest sciences.
• Scope to develop your own methodological focus and pursue publications and third-party funding initiatives, provided that these emerge from KIForst activities and pilot projects.
• Interdisciplinary collaboration with research groups across the Faculty as well as partners from data science.
• Access to high-performance scientific computing infrastructure and an interdisciplinary research environment across the Göttingen Campus.
• Flexible working arrangements and additional opportunities for mobile working in accordance with applicable regulations. Regular presence in Göttingen is expected for consulting, networking and teaching activities.

• A successfully completed university degree at Master’s level or equivalent and a completed PhD in computer science, data science, computational science or a comparable quantitative discipline. Applications from candidates who are close to completing their PhD are welcome, provided that completion in the near future can be reliably demonstrated.
• Demonstrated scientific expertise in machine learning and deep learning.
• Knowledge of explainable AI (XAI), uncertainty analysis and model validation.
• Strong Python skills, experience with deep learning frameworks such as PyTorch, and tools for processing complex and/or spatial data.
• Initial experience in teaching, workshop facilitation, supervision or scientific consulting.
• Very good written and spoken English; German language skills or a willingness to acquire them in the near future.
• Ability to communicate complex methods clearly, familiarise yourself with research questions outside your own discipline-including forest science-and collaborate effectively with different research groups.

Desirable qualifications

• Knowledge of HPC or cloud computing.
• Practical experience with computer vision.
• Experience with databases, research data management, GIS or geospatial data processing.