Researcher / Data Scientist (f/m/x) for AI-enabled Environmental Data Harmonization and Quality Control

Place of work

Working time

The job position is available for full-time or part-time employment.

Contract limitations

Salary

Contact

Your contact for any questions you may have about the job:
Ulf Mallast (ulf.mallast@ufz.de)
Martin Abbrent (martin.abbrent@ufz.de)

Your application

To ensure a fair selection process , please submit your application (cover letter, CV, and relevant supporting documents) via our online portal without a photo , age information, or details about your marital status.

Diversity and Inclusion

The UFZ values ​​diversity and is actively committed to ensuring equal opportunities for all employees, regardless of their origin, religion, beliefs, disability, age or sexual identity.
We welcome people who represent diverse backgrounds, identities and perspectives. We therefore particularly encourage people who are affected by structural discrimination to apply to us.

Your tasks

We are seeking a motivated highly Researcher or Data Scientist (f/m/x) with experience in scientific data management and applied AI. The successful candidate will undertake the following tasks:

• Design and operate scalable workflows for the ingestion, integration, deduplication and harmonization of heterogeneous environmental observation and research data, including traceable provenance, metadata enrichment and semantic annotation
• Develop and evaluate hybrid QA/QC methods combining established rule-based and statistical procedures with machine-learning approaches for anomaly detection, gap filling, sensor-drift detection and uncertainty-aware quality assessment
• Benchmark and evaluate AI-enabled QA/QC methods against established approaches using heterogeneous environmental and sensor-based time-series datasets, with particular emphasis on robustness, explainability and transferability across data sources and domains
• Transform research prototypes into tested, reproducible and maintainable data pipelines and services suitable for continuous data ingestion and operational use, including contributions to tools such as SaQC
• Prepare curated training and evaluation datasets and contribute to the assessment and adaptation of environmental and cross-domain foundation models
• Collaborate with environmental researchers, data managers, software engineers and international project partners, and contribute to technical documentation, reusable guidelines, training materials and scientific publications

We offer

• The freedom to master even the most demanding challenges between basic research and practical application
• The chance to work in interdisciplinary, international teams and benefit from a variety of perspectives
• First-class integration into national and international research networks to work together on global challenges
• Excellent research infrastructure and research data management to optimally support your work
• Flexible working hours and a wide range of options for balancing work and care responsibilities through our family office
• Competent support and advice for international colleagues arriving at the UFZ from the 'International Office'
• Special annual payment, capital-forming benefits and subsidized Germany Job Ticket
• A workplace in a vibrant region with a high quality of life and social and cultural diversity

Your profile

• A university degree, preferably at Master's or PhD level, in Data Sciences, Computer Sciences, Environmental Informatics, Geoinformatics, Environmental Sciences, or a related field
• Strong programming skills in Python or R and experience with established data-science and machine-learning libraries
• Experience in one or more of the following areas: time-series analysis, anomaly detection, data imputation, uncertainty quantification, machine learning or AI-enabled data analysis
• Experience with environmental, ecological, geoscientific, sensor-based or similarly heterogeneous scientific datasets
• Experience in developing reproducible data-processing workflows, including version control, testing and collaborative software development
• Familiarity with FAIR data principles, metadata standards, semantic technologies, APIs or interoperable research data infrastructures is an advantage
• Highly welcome: Experiences with and/or connections to EOSC
• Ability to work independently and collaboratively in an interdisciplinary and international environment
• Very good communication skills in English and German

Application deadline: August 2, 2026

LinkedIn @UFZ

Family Support

International Office

Accessibility

Diversity, Equity & Inclusion