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specifically interested in: Data-centric AI: Advancing machine unlearning, privacy-preserving techniques, and robust data curation. AI Safety : Ensuring robust alignment and safety in multi-agent LLM
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multi-agent LLM systems Efficiency: Streamlining large-scale model experimentation and training. Science of Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers
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, workshops or multi-partner activities is a plus Working knowledge of Python or R for data handling and figures; familiarity with git is a plus Familiarity with climate mitigation scenarios and the IPCC
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collaborations in the fields of turbine aerodynamics, heat transfer, acoustics and combustion research. The core competence currently lies in the experimental and numerical aerodynamic investigation of single
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extractions and analysis for national fossil fuel phase-out roadmap development Process multi-model scenario data (IAMC format) into consistent indicators, figures and statistics, with version-controlled