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Grant
Agency: Cordis | Branch: H2020 | Program: MSCA-ITN-ETN | Phase: MSCA-ITN-2014-ETN | Award Amount: 2.25M | Year: 2015

The use of visible light energy to induce chemical transformations constitutes an interesting and green activation mode of organic molecules. However, implementation of this energy source in organic synthetic methodologies and in the industrial production of fine chemicals has been challenging. The Photo4Future Innovative Training Network establishes a training network with five beneficiaries from academia and five beneficiaries from industry to tackle the challenges associated with photochemistry in a coherent and comprehensive fashion. In total 13 Early Stage Researchers will be recruited within the Photo4Future network. The network will provide them with opportunities to undertake research with the aim to overcome the current limitations towards the applicability and scalability of photochemical transformations. This will be achieved through a rational design of novel photocatalytic methodologies, improved catalytic systems and innovative photoreactors. Furthermore, the ESRs will be trained in the Photo4Future graduate school, covering training in scientific, personal and complementary skills. All the ESRs will perform two secondments, of which at least one is carried out with an industrial partner. Consequently, the ESRs will have improved career prospects and a higher employability. Due to the high degree of industrial participation, the Photo4Future network will provide an innovation-friendly environment where scientific results can grow and become products or services that will benefit European economies.


Grant
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: FETHPC-1-2014 | Award Amount: 3.91M | Year: 2015

Scalable machine learning of complex models on extreme data will be an important industrial application of exascale computers. In this project, we take the example of predicting compound bioactivity for the pharmaceutical industry, an important sector for Europe for employment, income, and solving the problems of an ageing society. Small scale approaches to machine learning have already been trialed and show great promise to reduce empirical testing costs by acting as a virtual screen to filter out tests unlikely to work. However, it is not yet possible to use all available data to make the best possible models, as algorithms (and their implementations) capable of learning the best models do not scale to such sizes and heterogeneity of input data. There are also further challenges including imbalanced data, confidence estimation, data standards model quality and feature diversity. The ExCAPE project aims to solve these problems by producing state of the art scalable algorithms and implementations thereof suitable for running on future Exascale machines. These approaches will scale programs for complex pharmaceutical workloads to input data sets at industry scale. The programs will be targeted at exascale platforms by using a mix of HPC programming techniques, advanced platform simulation for tuning and and suitable accelerators.

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