Helmholtz Zentrum für Umweltforschung UFZ
Researcher / Data Scientist for AI-enabled Environmental Data Harmonisation and Quality Control(m/w/x)
Vollzeit/Teilzeitmit HomeofficeKeine Angabe
Leipzig
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HEHelmholtz Centre for Environmental Research UFZ
.IDEAS PhD Researcher(m/w/x)
Leipzig, Dresden
ab USD 3.200 - 3.800 / Monat
Vollzeitmit Homeoffice
AI/ML
Data Science
Nejo KI-Zusammenfassung
Developing interpretable, robust ML/AI forecasts for flood events at environmental research institute. Strong programming and data analysis skills required. 30 days vacation, remote work possible.
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Anforderungen
- Very good Master’s degree (MSc or equivalent) in relevant field
- Strong programming/data analysis skills
- Motivation for large, complex datasets and modern ML/AI methods
- Strong interest in interdisciplinary research
- Ability to collaborate in diverse teams
- Very good English skills (written and spoken)
Aufgaben
- Conduct original PhD research at the interface of data science and domain science
- Contribute to scientific publications and dissemination
- Participate in IDEAS training and cohort activities
- Attend seminars, coursework, and community events
- Develop physically interpretable, robust, and trustworthy data-driven forecasts of flood events
- Combine advanced deep learning and causal representation learning frameworks
- Use explainable AI to enhance flood event predictions
- Identify soil moisture–cloud feedback pathways using graph-based AI methods
- Integrate in-situ, satellite, reanalysis, and climate-model data
- Improve understanding of land–atmosphere feedbacks
- Design novel mini-protein binders against glioblastoma biomarkers
- Combine generative protein design, machine learning, and radiochemistry
- Experimentally validate computationally designed binders
- Translate binders into radiopharmaceutical probes for molecular imaging
- Predict the elimination of plant protection products by pollinator microbiomes
- Use data science and AI to protect pollinators
- Combine graph representation of chemical reactions with existing models
- Develop digital markers of tissue health from continuous metabolic sensing data
- Generate and expand biological measurements
- Learn and predict outcomes from physiological time-series data
- Support clinical decisions with novel digital markers
- Propose and develop self-initiated projects in Life Sciences & Health or Environmental Sciences
- Obtain support from two IDEAS PIs for Innovation Track projects
- Contact potential supervisors before submitting applications
Ausbildung
- Master-Abschluss
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- ML/AI methods
Benefits
Mehr Urlaubstage
- 30 days of vacation
Flexibles Arbeiten
- Remote work possible
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