Beschreibung

With more than 160 professions represented, the Geneva University Hospitals (HUG) are a reference institution at the international level. To learn more about our institution, take a few minutes to consult our retrospective by clicking here. The Medical and Quality Management Directorate is responsible for guaranteeing the quality and safety of medical services offered by the Geneva University Hospitals. The Infection Prevention and Control Service (SPCI) is tasked with controlling and preventing infections and reservoirs of resistant microorganisms in the hospital environment. The service is composed of infectious disease physicians with clinical and research functions, a team of specialized nurses, epidemiologists, scientists, and more. The service is also a World Health Organization (WHO) collaborating center for infection prevention and control and resistance. Antibiotic-resistant bacteria pose a major challenge for health systems. The Geneva University Hospitals are developing innovative approaches that combine hospital epidemiology, data science, and artificial intelligence to improve the early detection of carriers of multidrug-resistant bacteria. In this context, we are developing the SEARCH-AI project funded by the HUG Private Foundation, aiming to optimize targeted screening for carriers of multidrug-resistant bacteria (ESBL, CPE, VRE) upon admission and during hospitalization using machine learning models applied to clinical hospital data.As part of your internship, you will participate in the development and application of predictive models to identify patients at risk of carrying resistant bacteria. Main tasks include:- Pre-processing and cleaning of clinical hospital data (microbiology records, historical data);- Development and comparison of machine learning models (penalized logistic regression, LASSO, tree-based models, XGBoost);- Evaluation of model performance and interpretability analysis (SHAP or similar methods);- Contribution to the discussion of the pipeline with a multidisciplinary team (clinicians, epidemiologists, scientists).Depending on the project’s advancement, the internship may lead to a scientific publication or conference presentation. This internship is intended for final-year students (Master's or engineering school) with a background in data science, machine learning, and applied statistics. You should have good command of Python or R for data analysis, knowledge in supervised machine learning, and solid fundamentals in statistics. Experience with large databases and a strong interest in health-related AI or biomedicine will also be beneficial. Furthermore, experience with medical or biological data, knowledge in epidemiology or public health, as well as familiarity with model interpretability methods will be considered a plus. Proficiency in English is also an asset.Start date: To be agreed uponNumber of positions: 1Activity rate: 100%Type of contract: 6-month fixed-term contract subject to agreementDeadline for inquiries: Send an emailYour application must include a cover letter demonstrating your motivation, your CV, copies of diplomas, and documents required for the position and the last 2 transcripts. The vacancy is open to both women and men. Applications submitted via email are considered. Paper applications and emails will not be processed. jida4c3434aen jit0414aen jpiy26aen