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Post-Doctoral Research Fellow in Biomedical Data Science Position description: Applications are invited for a postdoctoral research position in Biomedical Data Science. This is a joint initiative between the Host-Pathogen Interactions research group led by Prof Rachel McLoughlin at the School of Biochemistry and Immunology www.tcd.ie/Biochemistry/research/r_mcloughlin.php and the Discipline of Statistics and Information Systems, School of Computer Science and Statistics, Trinity College Dublin (contacts Prof. Brett Houlding and Prof. Arthur White) https://www.scss.tcd.ie/disciplines/statistics/. The successful applicant will lead an exciting new project that aims to develop novel statistical / machine learning tools to integrate clinical and immunological data, which will identify immune parameters predictive of infection outcomes during bloodstream infection with the human pathogen Staphylococcus aureus (MRSA). We are looking for a highly motivated and ambitious candidate with a strong background in mathematical modelling, data science and statistics, ideally in the context of a biological setting. Both research teams already include post-doctoral researchers and PhD students. The new position will actively support PhD students and undergraduate students in laboratory work, present at national and international meetings, and publish in leading international statistical and immunological journals. Scientific Background: The WHO highlights the epidemic of antibiotic resistance in MRSA as a particular threat to society, strongly advocating for the development of alternatives to antibiotics. Over the past 15 years significant efforts have been made to develop an anti-MRSA vaccine, but to-date none have been successful. This is in part due to the fact that, so far, well-defined correlates of immunity have failed to be identified. It is imperative then that we identify specific immune phenotypes associated with positive/negative outcomes during invasive MRSA infection in patients to facilitate the development of next generation vaccines against this type of infection. Recent improvements and access to hardware measurement devices have now opened up opportunities to analyse human cells on a level never before available, providing fast access to multi-dimensional attributes of infected cells before, during and after infection. This improvement in technology has resulted in a state-of-the-art multiplicity of data that is now ripe for statistical development, exploration and exploitation, moving from analysis by eye, to the use and development of cutting edge statistical machine learning and intelligent decision making algorithms. Requirements: A PhD in Statistics, Computer Science, or other related quantitative modelling subject with a proven publication record. Experience in using supervised and unsupervised machine learning / statistical algorithms, e.g., regression trees, ensemble methods, model-based clustering etc. Experience with suitable software for data extraction and analysis (Python, R, SQL etc.). Ability to work in a multi-disciplinary setting. Appointment will be made at level 2A point 3 of the SFI salary scale at a starting salary of €39,138 for a period of 2 years Closing Date for applications: 31st January 2019 or until a suitable candidate is identified. For further inquiries or to apply please contact [log in to unmask] Applications should include a cover letter and CV giving the names and contact details of 2 referees. Trinity College Dublin is an equal opportunities employer. You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.