Position Overview

Researcher / Data Scientist (f/m/x) for AI-enabled Environmental Data Harmonisation and Quality Control at Helmholtz-Zentrum für Umweltforschung UFZ, Leipzig. Full‑time on site with partial remote work possible.

Working Conditions

Working time: 100% (39 h/week); full‑time or part‑time.
Contract: limited period.
Salary: TVöD public‑sector pay grade up to 13.

Responsibilities

Design and operate scalable workflows for 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 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 emphasis on robustness, explainability and transferability.
Transform research prototypes into 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.

Qualifications

University degree, preferably Master’s or PhD, in Data Sciences, Computer Sciences, Environmental Informatics, Geoinformatics, Environmental Sciences or related field.
Strong programming skills in Python or R and experience with established data‑science and machine‑learning libraries.
Experience in at least one 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.
High level of knowledge of EOSC is highly welcome.
Ability to work independently and collaboratively in an interdisciplinary and international environment.
Very good communication skills in English and German.

Benefits

Freedom to master demanding challenges between basic research and practical application.
Chance to work in interdisciplinary, international teams and benefit from a variety of perspectives.
Integration into national and international research networks.
Excellent research infrastructure and data management support.
Flexible working hours and support for balancing work and care responsibilities.
Competent support and advice for international colleagues from the International Office.
Special annual payment, capital‑forming benefits and subsidised Germany Job Ticket.
Workplace in a vibrant region with high quality of life and social and cultural diversity.

Diversity and Inclusion

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

Application Deadline

#J-18808-Ljbffr

Helmholtz-Zentrum für Umweltforschung (UFZ)