MCPs

In the Brain-IT MCP we use and enhance e-science approaches to address plasticity phenomena over different temporal and spatial scales. We develop and enhance e-science components in multi-scale modelling and analysis of brain plasticity, bridging molecular-, cellular-, synaptic-, network- and system level perspectives.

The research in e-Science is becoming increasingly data-driven, using methods from the neighboring fields of Data Science, Machine Learning, and AI. The aim of the Data Science MCP is to bring together researchers interested in data-driven methods, in order to spread and develop knowledge among the different application areas of SeRC.

New innovative materials are a crucial part of many emerging technologies. The integration of materials science with data-driven methods stands to be disruptive for the field of materials design. The Data-driven computational materials design (DCMD) MCP coordinates our efforts in this new exciting research direction.

Cancer prevention is a subject with α deep socioeconomic impact. eCPC has built up integrated e-Science competencies in large projects on prostate, breast and cervical cancer. A range of e-Science experts (mathematics, statistics, bioinformatics, computer science) work together with molecular scientists, epidemiologists and clinicians to develop a modularized e-Science framework for personalized screening and treatment.

Geophysical fluid dynamics modelling ranges from Direct Numerical Simulation to Earth System Modelling, with a common basis in numerical solution of  the equations of motion. From boundary layer turbulence to cloud formation, from ice sheet motion to the future climate under strong CO₂ forcing; in this MCP we bring together model developers and users across scales, and across application areas within the domain of modelling relevant for geophysical and complex flow dynamic.

SESSI’s goal is to advance science by improving performance towards the exascale of key scientific codes. To use exascale machines efficiently, new and advanced algorithms are needed for both acceleration (GPUs) and parallelization. Developing these requires deep and specialized computer science knowledge about algorithms, communication patterns and the hardware, which SESSI provides through application and computer science experts.

The goal of this MCP is to develop visual analytics environments tailored to large-scale, complex, and  dynamic data enabling interactive multi-scale analysis for knowledge discovery. A participatory design process involving domain and visualization experts is the core of the project ensuring relevance and practicability.