h3Scientific Assistant – Biomedical Data Science Spinal Cord Injury Research /h3p80%-100%, Zurich, fixed-term /ppThe Biomedical Data Science Lab investigates data-driven solutions for healthcare applications with a focus on neurological conditions and systemic infections such as sepsis. At the core of our research is the collaboration across disciplines spanning expertise in medicine, biology, computer, and data science. We seek a motivated scientific assistant to join this growing team and contribute to interdisciplinary research partnerships. /ph3Project background /h3pThe Biomedical Data Science Lab at ETH Zurich invites applications for a Scientific Assistant to support our data-intensive projects in biomedical imaging and clinical research. This position offers an excellent opportunity to work at the forefront of biomedical data science, contributing to key projects including data curation, image segmentation, and tool development for clinical research applications. The scientific assistant will support the Biomedical Data Science Team for a range of application projects, with a particular focus on our work in pediatric sepsis, lower back pain, and pediatric neurooncology. /ph3Job description /h3pWe are looking for a Scientific Assistant with a strong background in statistics, machine learning, and biomedical data science to support our research using the European Multicenter Study about Spinal Cord Injury (EMSCI) and related SCI databases. /ppThe position combines hands‑on data analysis and scientific project support with the coordination of scientific activities within our research network. The successful candidate will work closely with researchers, clinicians, and international collaborators to analyse large-scale longitudinal SCI datasets and contribute to scientific publications and meetings. /ph3Your key responsibilities will include: /h3ulliConduct statistical and machine‑learning analyses using EMSCI and related SCI datasets. /liliDevelop and validate predictive models of neurological recovery and functional outcomes after SCI. /liliPerform data preprocessing, quality control, harmonisation, feature engineering, and exploratory data analysis. /liliApply appropriate statistical methods, including regression, longitudinal data analysis, survival analysis, and machine‑learning approaches. /liliDevelop reproducible analytical workflows in R and/or Python. /liliContribute to scientific publications, reports, presentations, and grant applications. /liliCollaborate with clinicians, neuroscientists, statisticians, and data scientists across participating SCI centres. /liliSupport the organisation and coordination of scientific meetings, workshops, consortium meetings, and other research activities, including scheduling, preparation of agendas and materials, communication with participants, and follow‑up. /liliAssist with the coordination of ongoing collaborative projects and follow‑up of scientific activities and deliverables. /li /ulh3Profile /h3ulliMSc or equivalent degree in statistics, biostatistics, data science, biomedical engineering, computer science, epidemiology, neuroscience, or a related discipline. /liliStrong background in statistics and/or machine learning. /liliExperience working with longitudinal and/or clinical datasets. /liliStrong programming skills in R and/or Python. /liliExperience with statistical modelling, data visualisation, and reproducible research. /liliFamiliarity with machine‑learning methods such as random forests, gradient boosting, neural networks, or related approaches. /liliPrevious experience working with EMSCI data or other large SCI databases is highly desirable. /liliKnowledge of SCI‑specific clinical and neurological outcome measures (e.g., ISNCSCI/ASIA, SCIM) is an advantage. /liliExperience working with multicentre clinical datasets is an advantage. /liliFluent in English and proficient in German, both written and spoken. /liliExcellent organisational and communication skills. /liliAbility to work independently and reliably while collaborating effectively in a multidisciplinary and international research environment. /liliInterest in SCI research and the application of data science to clinical research. /li /ulh3We offer /h3ulliAn 80‑100% position within the Biomedical Data Science Lab at ETH Zurich. /liliThe opportunity to work with EMSCI, one of the major longitudinal SCI research databases, and related international datasets. /liliA highly interdisciplinary environment at the interface of clinical neuroscience, statistics, machine learning, and biomedical data science. /liliClose interaction with leading SCI researchers and clinical centres in Switzerland and internationally. /liliThe opportunity to contribute to high‑quality scientific publications and international collaborative projects. /liliA varied role combining hands‑on quantitative research with scientific coordination and project management. /liliA stimulating research environment at ETH Zurich, with the lab based in Schlieren. /li /ulh3We value diversity and sustainability /h3pIn line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate‑neutral future. /ppCurious? So are we. /ppStart date: October 1st, 2026 /ppEmployment: 80–100% /ppLocation: ETH Zurich Switzerland /ppDuration: temporary, 1 year (with possibility of extension by 1 year) /ppFurther information about the BMDS lab can be found on our website. /ppQuestions regarding the position should be directed to Prof. Catherine Jutzeler ( ). /ppWe would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence. /ph3About ETH Zürich /h3pETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting‑edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow. /p #J-18808-Ljbffr
