TranSYS is a multi-partner European Training Network (ETN) under the European Union programme Horizon 2020. TranSYS aims at exploiting large and complex datasets, to advance Precision (Personalised) Medicine in several disease areas. Our project will advance the state of the art on biomarker discovery, improve understanding of disease-specific molecular mechanisms and target identification for optimal diagnostics, disease risk and treatment management, refine data generation and management, thereby leading to improved clinical study design, clinical sampling and more targeted therapeutics. TranSYS brings together international leaders in Preclinical Science & Molecular Medicine, Systems Analytics, and Targeted Therapeutics, from academia and industry. TranSYS will internationalise the participating ESRs (Early Stage Researchers), and advance emerging research areas, combining wet-lab, clinical and Big Data resources with computational and modelling knowhow.

Your responsibilities

• Developing and demonstrating data mining and A.I. tools to better understand patient heterogeneity and assist patient stratification
• Collecting links to online data repositories that offer access-free health-relateddata;
• Structuring of databases for related samples;
• Design and development of a data mining tool that access, collect and harmonise data via APIs (Application Programming Interfaces), making it easily usable for further downstream interpretation/analysis;
• Implementation of an AI algorithm that classifies automatically a specific sample and/or detects a potential misclassification;
• Undertaking proof of concept case studies comparing DECIPHER CNV data of patients suffering from developmental neurological malformations with ENCODE data, using metabolic liver pathologies;
• To disseminate your results using the TranSYS network’s information channels.

• Master degree or equivalent in Computational Biology, Bioinformatics, Systems Biology, Bio(techno)logy or related field;
• Solid knowledge of bioinformatics;
• Basic knowledge of statistics;
• Experience in discussing results of computational analyses in collaboration with wet-lab scientists;
• Good programming skills in R or Python and preferably experience in at least one other programming language (Perl, Java, C++);
• Knowledge of semantic web technologies (XML, RDF, SPARQL) would be an asset;
• Basic knowledge of AI/Machine Learning algorithms;
• Data base knowledge (SQL, RDF);
• Familiarity with publicly available biological data resources and exploiting them (by using APIs)
• Interest in blockchain technology;
• A pragmatic and solution oriented working style;
• Enthusiasm for communicating with various project partners;
• Solid written and oral communication skills in English.

• Attractive three years full time PhD position starting latest March 1st, 2020;
• Participation in an international innovative training network;
• Flexible working hours;
• An excellent working atmosphere in a dynamic and multicultural team with flat hierarchies.

Your supervisor would be Prof. Dr. V. Martins dos Santos, Wageningen University/CEO LifeGlimmer and the co-supervisors: Prof. Dr. D. Rozman, University of Ljubljana & Prof. Dr. P. van der Spek, Erasmus University Medical Center Rotterdam.

LifeGlimmer GmbH – translating science to the public. We are a dynamic SME based in Berlin. We analyse, visualise and model data, focusing on the life sciences and systems biology. We contribute to understand disease and optimise treatments. Our business activities comprise contract research, consulting and training.