The Scientific Computing Centre (SCC) is a central scientific institution at KIT that performs tasks related to research, teaching, and innovation and provides comprehensive services within KIT and to external parties.

This research project focuses on the application of AI models in Earth System Science (efficiently replacement of a chemistry-climate simulation from an Earth System Model (ESM) by an AI-based approach) and the development of mathematically rigorous methodes for interpretable AI modeling and systematic hyperparameter optimization. In detail:

• Development of a concept to replace the chemistry climate simulation of the ESM with a suitable AI model. This includes testing and selecting appropriate AI architectures (e.g., RNNs, CNNs, PINNs, Transformers), identifying relevant input features, applying dimensionality reduction techniques, and performing hyperparameter tuning (learning rate, number of layers, regularization strength).
• Training the AI model using a dataset from long-term ESM simulations (from 1979 to 2024), followed by running simulations with the trained AI model.
• Comparative analysis of AI-based simulations versus traditional ESM simulations, assessing accuracy  and performance. 
• Moving beyond a black-box approach, the project aims to achieve a mathematical understanding of the AI model. This understanding should be used to develop systematic methods for optimizing the hyperparameter tuning, grounded in the mathematical areas of optimization theory, statistical learning theory, and explainable AI.
• The theoretical analysis of this hyperparameter optimization - such as studying convergence properties, regularization effects, and sensitivity analysis - constitutes the core mathematical challenge of the project.

This position offers the opportunity to earn a doctoral degree while working in the CSMM research group led by Prof. Dr. Martin Frank and being part of the KIT's KCDS graduate school. 

• Completed studies (master) in mathematics
• Knowledge of current deep learning frameworks (e.g., PyTorch or Tensorflow) and current AI models
• Interest in optimization theory, statistical learning theory or explainable AI
• Ability to work and publish in a targeted and scientific manner
• Good communication and presentation skills and willingness and ability to work a team
• Good communication skills in German or English

The SCC offers you an exciting and varied job within an agile team as well as a wide range of training opportunities and flexible and family-friendly working time models. For more information about SCC visit: https://www.scc.kit.edu/en/aboutus/working-at-scc.php

We are looking forward to your application including motivation letter, CV and certificates.