HPC & AI in Life Science (Agentic Modeling & Simulation Workflows for the Life Sciences)
HPC-based Simulation of Agent-Based Models & Complex Networks, Opinion Dynamics & Polarization
Computational Materials Science
FPGA-based deep learning acceleration, AI compiler frameworks, & sparsity-aware NPU design
Algorithmic differentiation for high-performance scientific computing
Numerical Modelling of Iron Oxide Reduction
Improving Energy Efficiency in Scientific Workloads on AI Centered HPC Hardware through Reduced Precision
Trustworthy and Scalable Agentic Workflows through Knowledge Grounding and Explainable Decision Backing
Convergence of Cloud Computing, High-Performance Computing, and the Internet of Things
Correctness Analysis for Parallel Programming Models
Designing safer analgesics: protease-resistant opioid peptides and their potential in Alzheimer's disease
Machine-learning reconstruction of moment equations for shock-dominated rarefied gas flows
Resolution-switching in biomembrane simulations
DNA recognition by DNA-processing proteins
Reliable and Secure Data Science Workloads in Data Centers
Robust Reinforcement Learning for Large Language Models: Geometry-Regulated Policy Optimization, Process Reward Integration, and Agentic AI
Computational investigation of reactivity & electronic structure of the transition metal complexes involved in dinitrogen activation process
Data-Driven Optimization Strategies for Materials Design
Sparse Neural Networks and Sparse Linear Algebra
Parallel Multigrid Algorithms for Systems of Partial Differential Equations.
A new RTL language for the design of high-performance FPGA accelerators/Making large-scale low-level hardware design more feasible.
Datenstrukturen auf modernen GPU-Systemen/Data structures on modern GPU systems
Memories in Homeostatic Neural Networks Using Structural Plasticity.
Combinatorial SIMD Programming
Numerical solution of partial differential equations with multigrid and finite element methods.
Hybrid Modeling for Neuropathic Pain
Scaling Linear Solver of Phase Field Simulation Software PACE3D to large High Performing Computing System.
Teaching Neural Networks how to experiment.
Development of new electronic structure methods based on Adiabatic-connection fluctuation-dissipation.
Understanding Behavior in Parallel I/O Using Deep Learning.
Hybrid methods for finding transition manifolds in complex Systems.
Optimization of Workload Mapping to heterogeneous HPC System landscape.
Machine Learning Based Optimization of Photonic Links
Machine-learning Interatomic Potential for Heat Transport
Structure-preserving numerical methods for the Vlasov equation without a phase-space mesh
