BioNMR Lab Lead & Data Science Pipelines

University of Vienna; Wien; On-site; EUR 41,000 - 46,000; Full time

Benefits offered by this job

Competitive salary

34 h/week

Training courses

City-centre location

Childcare facilities

Diversity & inclusion

Job summary

The University of Vienna invites applications for the role of Lab Coordinator BioNMR Facility & Data Science Specialist. The position focuses on BioNMR facility operations, user support, and data pipeline development, combining instrument management with modern data science and coding.

The successful candidate will support a user base of researchers, contribute to metabolomics and AI-driven analysis, and help ensure safety, data integrity, and collaborative research across departments and

Qualifications

• Master's or PhD in biophysics, physical chemistry, bioinformatics, data science, chemistry or related field.
• Hands-on with NMR spectroscopy and data processing pipelines.
• Programming experience in Python and/or R.

Responsibilities

• Troubleshoot instrument/software issues and coordinate vendor support.
• Onboard new users and provide training for measurement procedures.
• Develop and maintain Python/R data pipelines for metabolomics and AI tasks.
• Integrate ML tools for spectral processing and automate workflows.
• Coordinate IT infrastructure, backups, and data management.
• Ensure safety, magnet training, and access controls.
• Collaborate on research projects with university groups and external partners.

Skills

NMR spectroscopy

Data pipelines

Python or R

Analytical workflows

Education

Master's or PhD degree

Tools

Python

R

NMR hardware

Job description

The University of Vienna invites applications for the role of Lab Coordinator BioNMR Facility & Data Science Specialist. The position focuses on BioNMR facility operations, user support, and data pipeline development, combining instrument management with modern data science and coding.

The successful candidate will support a user base of researchers, contribute to metabolomics and AI-driven analysis, and help ensure safety, data integrity, and collaborative research across departments and