Four PhD student positions are still vacant in the Marie Curie Innovative Training Network "Machine Learning Frontiers in Precision Medicine" (MLFPM).
Candidates with statistics or computer science background are especially encouraged to apply.
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The Marie Curie Innovative Training Network "Machine Learning Frontiers in Precision Medicine" (MLFPM) brings together leading European research institutes in machine learning and statistical genetics, both from the private and public sector, to train 14 early stage researchers.
The following 4 positions and projects are available:
ESR1 will work on "Machine Learning for Biological Network Analysis" with Karsten Borgwardt at ETH Zürich in Basel, Switzerland.
ESR6 will work on “Clinical decision support for precision medicine” with Tobias Heimann and Volker Tresp at Siemens Healthcare GmbH in Erlangen, Germany.
ESR7 will work on "Methodology for discovery and validation of omics-based predictors for follow-up data in large population-based biobanks" with Krista Fischer at the University of Tartu in Tartu, Estonia.
ESR10 will work on “Personalized health trajectories” with Antonio Artés at Universidad Carlos III de Madrid in Madrid, Spain.
More info:
https://euraxess.ec.europa.eu/jobs/397305
[https://euraxess.ec.europa.eu//sites/default/files/jobs_funding.jpg]<https://euraxess.ec.europa.eu/jobs/397305>
4 PhD Positions within the Marie Sk³odowska-Curie ITN "MLFPM”<https://euraxess.ec.europa.eu/jobs/397305>
euraxess.ec.europa.eu
The Marie Curie Innovative Training Network "Machine Learning Frontiers in Precision Medicine" (MLFPM) brings 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 develop and apply machine learning methods to health data. The goal is to reveal new insights into disease mechanisms and therapy outcomes, and to exploit the findings for precision medicine, which hopes to offer personalized preventive care and therapy selection for each patient.
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