Job
- Level
- Experienced
- Job Field
- Data
- Employment Type
- Part Time/Full Time
- Contract Type
- Temporary employment
- Location
- Leipzig
- Working Model
- Hybrid, Onsite
Job Summary
In this role, you will develop AI-enabled workflows for the harmonization and quality assurance of environmental data, manage data integration and validation using time-series datasets, and transform research prototypes into reproducible data pipelines.
Job Technologies
Your role in the team
- UFZ is the lead institution of the eLTER ESFRI process, aiming at the establishment of the Integrated European Long-Term Ecosystem, Critical Zone, and Socio-Ecological Research Infrastructure (eLTER RI, eLTER-RI.eu) in the context of the high-level strategic platform for the development of priority European RIs (European Strategy Forum on Research Infrastructures, ESFRI, https://www.esfri.eu/).
- The UFZ campus in Leipzig hosts the eLTER Head Office, which manages the eLTER ERIC legal entity and coordinates related eLTER projects and activities in close collaboration with the University of Helsinki.
- 165 scientific institutions in 28 countries and 26 national LTER networks have been collaborating in this process and creating the pan-European distributed physical network of > 250 highly instrumented ecosystem and socio-ecological research sites.
- Reliable environmental AI depends on trustworthy, harmonised and continuously quality-controlled data.
- At UFZ and eLTER, we develop scalable workflows that transform heterogeneous observations from distributed research sites and data platforms into interoperable, traceable and AI-ready data products.
- We are looking for a Researcher or Data Scientist to develop and operationalise AI-enabled workflows for the harmonisation, curation and quality assurance of environmental data and other applications.
- The position connects distributed environmental research infrastructures, continuously generated observation data and emerging AI models, translating methodological developments into reproducible and scalable workflows for scientific and operational use.
- Design and operate scalable workflows for the ingestion, integration, deduplication, and harmonisation 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.
This text has been machine translated. Show original
Our expectations of you
Education
- A university degree, preferably at Master's or PhD level, in Data Sciences, Computer Sciences, Environmental Informatics, Geoinformatics, Environmental Sciences, or a related field.
Qualifications
- Familiarity with FAIR data principles, metadata standards, semantic technologies, APIs or interoperable research data infrastructures is an advantage.
- Ability to work independently and collaboratively in an interdisciplinary and international environment.
- Very good communication skills in English and German.
Experience
- 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.
- Highly welcome: Experiences with and/or connections to EOSC.
This text has been machine translated. Show original
What we offer
- The job position is available for full-time or part-time employment.
- Remuneration according to the TVöD public-sector up to pay grade 13 including attractive public-sector social security benefits.
- 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 collaborate 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.
This text has been machine translated. Show original
Benefits
Work-Life-Integration
Topics that you deal with on the job
Job Locations
This is your employer
Helmholtz Zentrum für Umweltforschung GmbH
The Helmholtz Centre for Environmental Research - UFZ has built up an excellent reputation as an international competence centre for environmental sciences with its 1,100 employees. We are part of the largest scientific organisation in Germany - the Helmholtz Association. Our mission: We conduct research to achieve a balance between social development and long-term protection of our living standards - for sustainable development.
Description
- Company Type
- Established Company
- Working Model
- Hybrid, Onsite
- Industry
- Agriculture, Silviculture, Education System
Researcher or Data Scientist
- Location
- Leipzig
- Working Model
- Hybrid, Onsite
- Diversity
- Open for all genders
