4 Open Science Jobs in Wien
Stellenbeschreibung:
- Beratungstätigkeit und Hilfestellung für Forschende im Bereich Forschungsdatenmanagement und Open Science, u.a. Unterstützung bei der Erstellung von Datenmanagementplänen
- Konzeption und Entwicklung innovativer disziplinspezifischer Services im Bereich Forschungsdatenmanagement
- Awareness-Building für nachhaltiges Forschungsdatenmanagement
- Entwicklung von Prozessen, Policies und Standards für Forschungsdatenmanagement
- Konzeption und Durchführung von bedarfsorientiertem Training zu Forschungsdatenmanagement und Open Science
- Durchführung von Bedarfserhebungen unter Forschenden an der Fakultät
- Vernetzung in universitätsinternen, nationalen und internationalen Netzwerken und Initiativen
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Job description:
- Providing advice and support to researchers in the area of Research Data Management and Open Science, a.o. assisting them in writing data management plans
- Designing and developing innovative discipline-specific services in the area of Research Data Management
- Building awareness for sustainable Research Data Management
- Developing workflows, policies and standards for Research Data Management
- Designing and delivering needs-based training on Research Data Management and Open Science
- Conducting needs assessments among the faculty
- Networking in university, national and international initiatives
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Ihr Aufgabenbereich
- Doctorate related to the above requirements
- Strong background in optimization and partial differential equations
- Strong background in numerical mathematics and computing
- Machine learning skills are welcome
- English skills needed
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Your Tasks
- Develop and merge innovative approaches in spatial data encoding, continuous output representation, and multi-variable simulation with dynamic data integration (Retrieval Augmented Generation, RAG) to push the boundaries of spatial stochastic simulation research.
- Design, prototype, and test advanced transformer-based methods tailored to complex spatial and multi-variable data.
- Create robust training protocols to manage non-stationary data and develop strategies for continuous output (e.g., raw value predictions, Fourier decomposition).
- Implement tokenization and cross-attention techniques to efficiently handle multi-variable simulations.
- Collaborate with international partners and contribute to an environment that values scientific freedom, interdisciplinary work, and curiosity-driven exploration.
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