Supervisors
Supervisor Expertise ESR Supervision
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