MCPs

Brain-IT

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.

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Data-driven computational materials design (DCMD)

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.

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e-Science for Cancer Prevention and Control

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.

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SeRC Data Science

There is a rapid increase in methods making use of real-life data. This field of research is often denoted Data Science. There are different aspects of Data Science, presenting different methodological challenges – addressed with different mathematical and computer-scientific tools.

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SeRC Exascale Simulation Software Initiative – SESSI

SESSI’s goal is to advance science by improving performance towards the exascale of key scientific codes. Exascale computers will enable much larger computations than are currently possible, but most scientific codes are limited in scaling to 10,000-100,000 cores. Going beyond this requires deep and specialized computer science knowledge about algorithms, communication patterns and new hardware.

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Visual Data Analytics in e-Science Applications

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.

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