School for Simulation and Data Science (SSD)

Technical problems are increasingly solved with the help of simulation tools. In particular, the lack of qualified experts using supercomputers for the analysis and simulation of complex, data-intensive systems, obstructs the scientific development in this area. 

The aim of the School for Simulation and Data Science (SSD) is to train the next generation of young scientists in the area of simulation and data sciences on severals levels: providing programs for Master’s students, doctoral candidates, and postdoctoral researchers as well as support for junior research group leaders.

The SSD is the consolidation of the German Research School for Simulation Sciences (GRS) and the AICES Graduate School and represents the development of both schools under one roof.

Forschungszentrum Jülich and RWTH Aachen University decided to fund 15 positions for doctoral candidates within the SSD. The projects were chosen based on two competitive calls for proposals:

 

1) High Definition Analysis of Packed Bed Chromatography
Prof. Marek Behr (RWTH), Dr. Eric von Lieres (FZJ)

2) Generalized Langevin Equation-based Simulations for Mesoscale Description of Neural Cascades
Dr. Vania Calandrini (FZJ), Prof. Benjamin Stamm (RWTH)

3) Rigidity Theory-based Prediction of Protein Stability in Ionic Liquids and Seawater for Guiding Protein Engineering
Prof. Holger Gohlke (FZJ), Prof. Ulrich Schwaneberg, Dr. Mahdi Davari (RWTH)

4) Implant Topology Optimization for Improving Adhesion at the Bone-implant Interface
Prof. Roger Sauer (RWTH), Dr. Bo Perrson (FZJ)

5) Massively Parallel Adjoints
Prof. Uwe Naumann, Dr. Johannes Lotz (RWTH), Dr. Eric von Lieres, Dr. Jörn Ungermann (FZJ)

6) Efficient All-atom RNA and DNA Simulations
Prof. Giulia Rossetti, Dr. Sadipan Mohanty (FZJ), Prof. Ivan Costa (RWTH, UK Aachen) 

7) Prediction of ligand protein interactions and brain signaling cascades using machine learning approaches: olfaction as a test scenario
Jun. Prof. Mercedes Alfonso-Prieto, PhD(FZJ), Prof. Alejandro Giorgetti, PhD(FZJ), Prof. Dr. Marc Spehr(RWTH)

8) Understanding dendritic failure in all-solid-state batteries: An ab initio-informed numerical multiscale model framework
Dr. Stefanie Braun(RWTH), Dr. Claas Hüter(FZJ)

9) Advanced Deep Learning strategies for multimodal data integration in functional neuroimaging
Dr. Jürgen Dammers(FZJ), Prof. Dr. Irene Neuner(RWTH)

10) Renormalized equations of motion for neuronal networks
Jun. Prof. Dr. Moritz Helias(FZJ), Prof. Dr. Carsten Honerkamp(RWTH)

11) Data-driven analysis of medical and simulation data for improved patient treatment in rhinology
Dr. Andreas Lintermann(RWTH), Dr. Jenia Jitsev(FZJ), Prof. Dr. Morris Riedel(FZJ)

12) Machine learning-guided multiscale simulations to study ion channel clusters
Dr. Jan-Philipp Machtens(FZJ), Prof. Dr. Angelika Lampert(RWTH) 

13) Learning-to-learn: Hyperparameter optimization of spiking neuronal networks using HPC resources
Dr. Alexander Peyser(FZJ), Prof. Dr. Michael Herty(RWTH)

14) DeFRoSTEr: Deep fluids and their relation to subglacial tunnel valley erosion
Dr. Sönke Reiche(RWTH), PD Dr. Julia Kowalski(RWTH), Prof. Stefan Kollet, PhD (FZJ)

15) Accelerator architectures for DFT hybrid functionals
Dr. Daniel Wortmann(FZJ), Dr. Christian Terboven(RWTH)