Explore the words cloud of the MLFPM2018 project. It provides you a very rough idea of what is the project "MLFPM2018" about.
The following table provides information about the project.
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH
|Coordinator Country||Switzerland [CH]|
|Total cost||3˙638˙275 €|
|EC max contribution||3˙638˙275 € (100%)|
1. H2020-EU.1.3.1. (Fostering new skills by means of excellent initial training of researchers)
|Duration (year-month-day)||from 2019-01-01 to 2022-12-31|
Take a look of project's partnership.
|1||EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH||CH (ZUERICH)||coordinator||562˙553.00|
|2||MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EV||DE (MUENCHEN)||participant||505˙576.00|
|3||QLUCORE AB||SE (LUND)||participant||281˙982.00|
|4||ASSOCIATION POUR LA RECHERCHE ET LE DEVELOPPEMENT DES METHODES ET PROCESSUS INDUSTRIELS||FR (PARIS)||participant||274˙802.00|
|5||UNIVERSITE DE PARIS||FR (PARIS)||participant||274˙802.00|
|6||IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD||IL (PETACH TIKVA)||participant||263˙500.00|
|7||UNIVERSITE DE LIEGE||BE (LIEGE)||participant||256˙320.00|
|8||SIEMENS HEALTHCARE GMBH||DE (ERLANGEN)||participant||252˙788.00|
|9||FUNDACION PUBLICA ANDALUZA PROGRESO Y SALUD||ES (SEVILLA)||participant||250˙904.00|
|10||UNIVERSIDAD CARLOS III DE MADRID||ES (GETAFE (MADRID))||participant||250˙904.00|
|11||STACC OU||EE (TARTU)||participant||232˙069.00|
|12||TARTU ULIKOOL||EE (TARTU)||participant||232˙069.00|
|13||UNIVERSITE PARIS DESCARTES||FR (PARIS CEDEX 06)||participant||0.00|
|14||UNIVERSITE PARIS DIDEROT - PARIS 7||FR (PARIS)||participant||0.00|
|15||PHARMATICS LIMITED||UK (EDINBURGH)||partner||0.00|
|16||ROCHE DIAGNOSTICS GMBH||DE (MANNHEIM)||partner||0.00|
|17||TECHNISCHE UNIVERSITAET MUENCHEN||DE (MUENCHEN)||partner||0.00|
|18||UNIVERSITE DE RECHERCHE PARIS SCIENCES ET LETTRES - PSL RESEARCH UNIVERSITY||FR (PARIS)||partner||0.00|
Healthcare is entering the digital era: More and more patient data, from the molecular level of genome sequences to the level of image phenotypes and health history, are available in digital form. Exploring this big health data promises to reveal new insights into disease mechanisms and therapy outcomes. Ultimately, the goal is to exploit these insights for Precision Medicine, which hopes to offer personalized preventive care and therapy selection for each patient. A technology with transformational potential in analysing this health data is Machine Learning. Machine Learning strives to discover new knowledge in form of statistical dependencies in large datasets. Mining health data is, however, not a simple direct application of established machine learning techniques. On the contrary, the emerging population-scale and ultra-high dimensionality of health data creates the need to develop Machine Learning algorithms that can successfully operate at this scale. Overcoming these frontiers in Machine Learning is key to making the vision of Precision Medicine a reality. To meet this challenge, Europe urgently needs a new generation of scientists with knowledge in both machine learning and in health data analysis, who are extremely rare at a global scale. Our ETN’s goal is to close this gap, by bringing together leading European research institutes in Machine Learning and Statistical Genetics, both from the private and public sector, to train 14 early stage researchers. These scientists will help to shape the future of this important topic and increase Europe’s competitiveness in this domain, which will have severe academic and industrial impact in the future and has the potential to shape the healthcare and high tech sector in Europe in the 21st century.
|Central Data Repository for Open Research Data Pilot||Open Research Data Pilot||2020-02-18 15:57:27|
Take a look to the deliverables list in detail: detailed list of MLFPM2018 deliverables.
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The information about "MLFPM2018" are provided by the European Opendata Portal: CORDIS opendata.