| C. Alexandrou (UCY) |
HPC, LQCD |
Project 2: Efficient methods for traces of matrix functions
Project 7: Tensor network-based quantum computer simulator
Project 10: Precision lattice quantum chromodynamics matrix elements
Project 11: Improved computation of GPDs with learnable denoising approaches |
| H. Panagopoulos (UCY) |
LGT, LQCD |
Project 7: Tensor network-based quantum computer simulator
Project 8: Quantum computing and tensor networks for (2+1)D and (3+1)D QED
Project 11: Improved computation of GPDs with learnable denoising approaches |
| G. Koutsou (CyI) |
HPC, LQCD |
Project 4: Accelerating simulations of lattice gauge theories with equivariant flows |
| M. Nicolaou (CyI) |
ML |
Project 3: Machine learning for multigrid methods
Project 13: Complex wetting problems using neural networks |
| N. Savva (CyI) |
CFD |
Project 13: Complex wetting problems using neural networks |
| K. UM (TP-IMT) |
ML |
Project 5: Deep-data assimilation and deep-feature-based metric for turbulent flows
Project 12: Large eddy simulation models in a deep machine learning loop
Project 13: Complex wetting problems using neural networks |
| M. Desbrun (IP Paris) |
ML, CFD |
Project 5: Deep-data assimilation and deep-feature-based metric for turbulent flows
Project 12: Large eddy simulation models in a deep machine learning loop |
| R. Benzi (UTOV) |
HPC, CFD |
Project 13: Complex wetting problems using neural networks |
| L. Biferale (UTOV) |
HPC, CFD |
Project 5: Deep-data assimilation and deep-feature-based metric for turbulent flows
Project 12: Large eddy simulation models in a deep machine learning loop |
| M. Sbragaglia (UTOV) |
HPC, CFD |
Project 13: Complex wetting problems using neural networks |
| E. Lindahl (KTH) |
HPC, ML, Biology |
Project 6: Data-driven MD: Calculating free energies by learning from QM Potentials & cryo-EM data
Project 14: Accelerating QM/MM simulations via machine learning
Project 15: Improved free energy calculation and extreme scalability of molecular dynamics simulation |
| D. Pleiter (KTH) |
HPC, LQCD |
Project 1: Performance-portability of task-based programming models on modern HPC architectures |
| S. Montangero (UNIPD) |
QC, tensor networks |
Project 7: Tensor network-based quantum computer simulator
Project 8: Quantum computing and tensor networks for (2+1)D and (3+1)D QED
Project 9: Quantum, classical and quantum-inspired machine learning |
| P. Carloni (RWTH & FZJ) |
HPC, Biology |
Project 6: Data-driven MD: Calculating free energies by learning from QM Potentials & cryo-EM data
Project 14: Accelerating QM/MM simulations via machine learning
Project 15: Improved free energy calculation and extreme scalability of molecular dynamics simulation |
| G. Rossetti (RWTH & FZJ) |
HPC, Biology |
Project 15: Improved free energy calculation and extreme scalability of molecular dynamics simulation |
| A. Frommer (BUW) |
HPC, linear algebra |
Project 1: Performance-portability of task-based programming models on modern HPC architectures
Project 2: Efficient methods for traces of matrix functions
Project 3: Machine learning for multigrid methods
Project 10: Precision lattice quantum chromodynamics matrix elements |
| K. Kahl (BUW) |
ML, LQCD algorithms |
Project 2: Efficient methods for traces of matrix functions
Project 3: Machine learning for multigrid methods
Project 10: Precision lattice quantum chromodynamics matrix elements |
| P. Kessel (TUB) |
ML |
Project 4: Accelerating simulations of lattice gauge theories with equivariant flows
Project 9: Quantum, classical and quantum-inspired machine learning
Project 11: Improved computation of GPDs with learnable denoising approaches |
| K. R. Muller (TUB) |
ML |
Project 4: Accelerating simulations of lattice gauge theories with equivariant flows
Project 9: Quantum, classical and quantum-inspired machine learning |
