Simulation alongside theory and experiment is nowadays considered an integral part of scientific discovery. As computation speeds up and new technologies and instruments improve, data generation in all fields of science is rapidly increasing. As a consequence, researchers face new challenges: Data collection exceeds by far the capacity to validate, analyse, visualize, store, and curate the information contained. Additionally, traditional, single-scale, macroscopic models are becoming inadequate for the accuracy requirements of modern physical, biological and engineering applications that involve multiscale phenomena occurring over vastly different scales. Scientific and engineering disciplines have been at the forefront of applying simulation to study complex phenomena that in many cases involve multiple scales, such as turbulence and neurogenerative diseases. These range from exascale simulations in lattice QCD, where high-throughput reduction and analysis capabilities of the large volumes of data generated is needed for extracting information, to simulations at the molecular and neuronal networks level of the neuronal dysfunction associated with Parkinson’s disease (PD), which is the second most common, fatal neurodegenerative disease.
The overall goal of the project was to deliver an interdisciplinary EJD program in computational science, which educates students to best address the challenges posed by exascale computing and data-intensive science, producing computational science professionals strategically positioned to become leaders in both academia and industry. The main objectives of the program were:
1) Deliver an innovative educational program, which intertwines exascale computing and data methodologies with specific applications.
2) Design innovative interdisciplinary research projects that use simulation and data science a their underlying methodology. Every PhD research project involves close co-supervision by experts from three degree-awarding institutions bringing complementary expertise beyond that of a single institution.
3) Bridge the gap between academia and industry.
4) Contribute to the transformative transition in using simulation and data science to drive scientific breakthroughs across the fields of CFD, Computational Biology and lattice QCD. The ESRs were exposed to new methodologies and approaches driven by novel compute and data technologies and algorithms, learned to communicate with scientists across disciplines and in industry and shared scientific tools. They are part of a new generation of computational scientists in Europe who brought new insights in these fields by utilising extreme computing and data.
5) Promote pan-European doctoral structures in a multi-disciplinary field of research leveraging excellence across academic institutions in Europe. Delivering a multi-disciplinary curriculum and research program by pooling excellence across Europe created an optimal training platform, and retained talent in Europe as well as helped to spread competencies across Europe, strengthening its research capacity.
6) Disseminate the capabilities enabled by simulation and data science and the results of the project. A rich dissemination program was implemented by all participants of the project targeting students, researchers at academia and industry, and the general public.
