Scientific Assistant - Biomedical Data Science & Spinal Cord Injury Research

The 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. 

Project background

The 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.

Job description

We 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.

The 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.

Your key responsibilities will include:

• Conduct statistical and machine-learning analyses using EMSCI and related SCI datasets.
• Develop and validate predictive models of neurological recovery and functional outcomes after SCI.
• Perform data preprocessing, quality control, harmonisation, feature engineering, and exploratory data analysis.
• Apply appropriate statistical methods, including regression, longitudinal data analysis, survival analysis, and machine-learning approaches.
• Develop reproducible analytical workflows in R and/or Python.
• Contribute to scientific publications, reports, presentations, and grant applications.
• Collaborate with clinicians, neuroscientists, statisticians, and data scientists across participating SCI centres.
• Support 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.
• Assist with the coordination of ongoing collaborative projects and follow-up of scientific activities and deliverables.

Profile

• MSc or equivalent degree in statistics, biostatistics, data science, biomedical engineering, computer science, epidemiology, neuroscience, or a related discipline.
• Strong background in statistics and/or machine learning.
• Experience working with longitudinal and/or clinical datasets.
• Strong programming skills in R and/or Python.
• Experience with statistical modelling, data visualisation, and reproducible research.
• Familiarity with machine-learning methods such as random forests, gradient boosting, neural networks, or related approaches.
• Previous experience working with EMSCI data or other large SCI databases is highly desirable.
• Knowledge of SCI-specific clinical and neurological outcome measures (e.g., ISNCSCI/ASIA, SCIM) is an advantage.
• Experience working with multicentre clinical datasets is an advantage.
• Fluent in English and proficient in German, both written and spoken.
• Excellent organisational and communication skills.
• Ability to work independently and reliably while collaborating effectively in a multidisciplinary and international research environment.
• Interest in SCI research and the application of data science to clinical research.

We offer

• An 80–100% position within the Biomedical Data Science Lab at ETH Zurich.
• The opportunity to work with EMSCI, one of the major longitudinal SCI research databases, and related international datasets.
• A highly interdisciplinary environment at the interface of clinical neuroscience, statistics, machine learning, and biomedical data science.
• Close interaction with leading SCI researchers and clinical centres in Switzerland and internationally.
• The opportunity to contribute to high-quality scientific publications and international collaborative projects.
• A varied role combining hands-on quantitative research with scientific coordination and project management.
• A stimulating research environment at ETH Zurich, with the lab based in Schlieren.

Working, teaching and research at ETH Zurich

We value diversity and sustainability

In 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 .

About ETH Zürich

ETH 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.

We look forward to receiving your online application with the following documents:

• CV / résumé
• Short application video
• Cover letter (including a link to the video)
• Employment references and diplomas/certificates

In the video, please briefly introduce yourself and address the following:

Imagine that you have just joined our lab and are asked to use EMSCI data to investigate predictors of neurological recovery after spinal cord injury. How would you approach this research question?

Please briefly explain:

• How you would define an appropriate clinical outcome;
• Which predictors you would consider and why;
• How you would handle longitudinal data and missing values;
• Which statistical and/or machine-learning methods you would consider
• How you would validate and interpret the results.

No actual analysis is required. We are primarily interested in your scientific reasoning, statistical understanding, and ability to communicate clearly.

Candidates who speak German are encouraged to submit the video in German. Candidates who do not speak German may submit the video in English.

The video should be recorded by the applicant. AI-generated voices or avatars should not be used. Slides or other visual aids may be used if helpful.

We would like to get to know you better and therefore ask you to submit a short, three-minute video. We do not expect a professionally produced video; a recording made with a smartphone is perfectly sufficient. Please then upload the video to a platform of your choice (Dropbox, YouTube, Vimeo, Google Drive, etc.) and include the relevant URL link in your cover letter.

Start date: October 1st, 2026
Employment: 80–100%
Location: ETH Zurich Switzerland
Duration: temporary, 1 year (with possibility of extension by 1 year)

Further information about the BMDS lab can be found on our website .

Questions regarding the position should be directed to Prof. Catherine Jutzeler (E-Mail auf jobs.nzz.ch ansehen).

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.