Jobs at SCC

• Scientific Associate (f/m/d) in Computer Science with the opportunity for a part-time PhD in the Research Group Exascale Algorithm Engineering
• Scientific Researcher (f/m/d) AI-Based Replacement of Chemistry-Climate Simulations with a Mathematical Focus on Model Understanding and Optimization - PhD Position at Faculty of Mathematics 75 % part-time -

Scientific Associate (f/m/d) in Computer Science
with the opportunity for a part-time PhD in the Research Group Exascale Algorithm Engineering

Category

Job description

The Scientific Computing Center is the Information Technology Center of KIT.

The Research Group Exascale Algorithm Engineering of SCC works at the interface of algorithmics, parallel computing, and applications in networked systems. Our teaching is delivered as part of the Institute of Theoretical Informatics.

In the focus of this position there is research on parallel discrete algorithms that have a strong foundation in theory and are practical at the same time. You can find selected examples of our previous research work at https://eae.iti.kit.edu/26.php.

Tasks associated with the position are:

• Development of novel algorithmic methods at the interface of discrete algorithms and high-performance computing
• Investigation of the algorithms’ theoretical properties
• Research software engineering (implementation of the developed algorithms)
• Empirical investigation of use cases
• Scientific exchange and publication of results

This position offers the possibility to acquire a PhD degree, which is encouraged.

Personal qualification

Job requirements:

• A very good university degree (Master's level) in computer science (or a closely related field, e.g., applied mathematics)
• Good knowledge of the design, as well as in the theoretical and empirical analysis of discrete algorithms and data structures, and in their implementation; particularly desirable are graph algorithms and C++ as a programming language
• Knowledge of parallel programming (e.g. with MPI or for GPUs) is of great interest
• Very good English skills; good German skills are an advantage but not required
• Self-motivation, ability to work in a team and a strong commitment to excellent scientific research, including interdisciplinary work

You can expect individual supervision, access to modern hardware for parallel computing, and further infrastructure for excellent scientific research.

Curious about an exciting and versatile role in an agile team? Discover more about SCC as your professional place to be: https://www.scc.kit.edu/en/aboutus/working-at-scc.php

Scientific Researcher (f/m/d) AI-Based Replacement of Chemistry-Climate Simulations with a Mathematical Focus on Model Understanding and Optimization
- PhD Position at Faculty of Mathematics 75 % part-time -

Category

Job description

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. 

Personal qualification

• 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.