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Faculty of Mathematics and Natural SciencesIntelligent Methods for Earth System Sciences

Machine Learning Scientist for the KPA-IMfESS

Prof. Dr. Aleksandar Bojchevski

Computer Science

Motivation 

The scientific goals of KPA-IMfESS hinge on advancing and integrating cutting-edge computational and machine learning (ML) methods into Earth System Sciences (ESS). While many researchers within the KPA already work with ML approaches, there is currently no dedicated ML coordinator or research engineer supporting sustainable model development, cross-group knowledge transfer, and translation of innovation in AI into ESS workflows. The ML Scientist position will create a central enabling function for the KPA, accelerating progress towards developing the next generation of AI-enabled ESS methods. The high-level goal of this position is to:

  • Foster and support interdisciplinary collaborations across Earth System Sciences, Computer Science, and AI
  • Enhance KPA competitiveness for large-scale proposals (SFB/TRR, EXC, …)
  • Help to train and support early-career researchers and enhance teaching innovation
Potential tasks

1. ML/AI Expert Support & Consulting for ESS Groups

  • Advise on ML model design, improvement, debugging, and deployment
  • Support FAIR, open, and reproducible research practices, and best practices for ML on HPC
  • Support deployment of scalable ML pipelines (HPC, cloud, hybrid workflows)

2. Shared Research Software & Resource Management

  • Build and curate a shared KPA software repository (models, data pipelines, benchmarks)
  • Standardize ML workflows and documentation, maintain ESS-relevant shared codebases

3. AI-Enhanced Research Processes

  • Integrate state-of-the-art AI tools (e.g. LLM assistance, synthetic data) into ESS research
  • Provide training on responsible and efficient AI use

4. Capacity Building & Teaching Innovation

  • Support the use of ML in courses, and help co-organise summer schools and hackathons
  • Contribute to interdisciplinary teaching formats bridging AI & ESS

5. Strategic Grant Preparation & Scientific Networking

  • Support drafting and coordination of large-scale proposals (SFB/TRR, EXC)
  • Foster joint projects with regional & international partners, including ELLIS and Helmholtz
     
Expected Outcomes. Some of the expected outcomes include:

  • Increased quality, robustness, and impact of ML-powered ESS research
  • Shared ML infrastructure and reusable research software artifacts
  • Enhanced attractiveness of KPA-IMfESS to funding bodies

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