Funding for research with Azure services

Microsoft Azure is a platform providing various cloud services, such as virtual servers or services with artificial intelligence. Employees of the University of Vienna may use these services provided by the ZID for the purposes of research, subject to a fee at special conditions. More information about Microsoft Azure

In order to support research activities in Azure, the ZID offers financial support for the calendar year 2025. A total of 20,000.00 euros is available. Up to 5,000.00 euros will be awarded per project.

Projects with one of the following characteristics will be prioritised when the funding is awarded:

  • They work with hybrid approaches (combined use of Azure services with local infrastructure)
  • They use Azure services with artificial intelligence
  • They use Azure services for which the ZID does not offer alternative IT services

Application requirements

The applicant must:

  • have a current employment with the University of Vienna and an active u:account
  • be entitled to order Microsoft 365 via the self-service portal
  • accept the Privacy Policy and Terms of Use for Microsoft Azure, see servicedesk form Microsoft Azure bestellen (ordering Microsoft Azure, in German)

Conditions of funding

  • The team Coordination Digital Transformation of the ZID decides on funding. If necessary, it consults with peer reviewers.
  • The amount of funding granted per project will be deducted monthly from the costs incurred for Azure over the period of use until 31.12.2025 on a pro rata basis.
  • Costs that exceed the granted funding amount or are incurred after the end of the funding must be covered by a cost centre available for the project.
  • The ZID is responsible for setting up the project environment in Azure, for onboarding and assigning user authorisations. Support for the technical implementation of the project is not offered.
  • Personnel resources are explicitly not funded.
  • Projects that have already been funded in 2024 are excluded from funding.
  • After the end of the funding period, the Azure environment provided and the resources contained therein remain available to users. Subsequent use of the services is possible and desired.

Timetable

  • 28.10.– 31.12.2024: Application for funding
  • 01.–12.01.2025: Internal review of applications and possible queries 
  • From 13.01.2025:
    Announcement
    of funded projects by e-mail
    Setup of Azure environments by the ZID, onboarding of users
  • From beginning of February 2025: Implementation of the projects
  • September 2025: Submission of interim report
  • December 2025: Submission of final report

Applying for funding

The application deadline for funding has expired.

Funding 2024

Funded research projects

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  • Marina Dütsch | FLEXWEB

    Organisational unit: Department of Meteorology and Geophysics

    Abstract (in German):

    Flexpart (FLEXible PARTicle dispersion model) ist ein numerisches Modell, das die Ausbreitung von Gasen und Aerosolen in der Atmosphäre simuliert. Das Ziel dieses Projekts war, ein Web Service (FLEXWEB) entwickeln, bei dem Flexpart über eine Webseite laufen gelassen werden kann. Flexpart sollte mit Hilfe eines Kubernetes Clusters Trajektorien berechnen und den Usern diese Ergebnisse leicht zugänglich machen. Ein wichtiger Schritt dabei war die Containerisierung der Arbeitsschritte und diese mit den relativen großen Eingangsdaten abzustimmen. Eine erste Version von FLEXWEB haben wir erfolgreich zum Laufen gebracht, allerdings ist es uns nicht gelungen, das Service skalierbar zu machen, so dass mehrere User es gleichzeitig nutzen können

    Flexpart Entwicklung an der Universität Wien

  • Wolfgang Klas | FactCheck

    Organisational unit: Multimedia Information Systems research group, Faculty of Computer Science

    Abstract:

    FactCheck is an ongoing research project conducted by the Research Group on Multimedia Information Systems within the Faculty of Computer Science. The project aims to identify conflicts in data available on the web. These conflicts may exist in various formats, such as textual (e.g., paragraphs in HTML documents) or multimedia (e.g., news segments in videos). To extract this conflict information, we will employ a combination of Semantic Web approaches (e.g., structured data) and state-of-the-art artificial intelligence technologies (e.g., named entity recognition and entity linking). The comparison processes for this information will involve both human intelligence and feedback, which is why we will also investigate methods for user identity and user management, such as Azure Entra ID. We are considering a hybrid approach to deploying the FactCheck prototypes. This will involve using both scalable Azure services (e.g., cognitive services like AI Video Indexer and user management solutions) and the existing on-premises infrastructure (e.g., virtual machines or databases) at the University of Vienna. This approach aims to achieve an appropriate balance between security, privacy, and cost. Some components may be containerized to ensure that the deployment remains highly flexible and modular, facilitating easier deployment on both Azure and local infrastructure.

  • Oliver Wieder | Revolutionizing Olfactory Perception Mapping: A Contrastive Learning Graph Neural Network Approach

    Organisational unit: Department of Pharmaceutical Sciences

    Abstract:

    Abstract will be added soon

  • Abert Claas | Very Largescale Distributed Micromagnetic Research Tools

    Organisational unit: Physics of Functional Materials

    Abstract (in German):

    Im Rahmen des Projekts wurde untersucht, wie gut sich die Microsoft Azure Cloud für wissenschaftliche Berechnungen im Bereich der Mikromagnetik eignet. Dafür wurden verschiedene virtuelle Maschinen getestet, sowohl mit Prozessoren als auch mit Grafikprozessoren. Ein besonderer Fokus lag auf der Nutzung von kostengünstigen Spot Instanzen, die jedoch gelegentlich unterbrochen werden können. Um die Arbeit mit den Simulationen zu vereinfachen, wurden hilfreiche Tools entwickelt, die Abläufe automatisieren und so Zeit sparen. Während des Projekts konnten außerdem mikromagnetische Simulationen zur Optimierung von magnonischen Geräten erfolgreich durchgeführt werden, die wertvolle Erkenntnisse lieferten. Anstelle ursprünglich geplanter Studien zur Nutzung mehrerer Grafikprozessoren lag der Schwerpunkt darauf, die Cloud-Lösungen umfassend zu bewerten. Zusätzlich wurde eine Bachelorarbeit verfasst, die zur Entwicklung der Automatisierungstools beitrug. Das Projektbudget von 2.000 EUR wurde fast vollständig genutzt, und die Ergebnisse zeigten, dass die Microsoft Azure Infrastruktur ein großes Potenzial für wissenschaftliche Anwendungen bietet.

  • Xin Huang | selscape: Automated and Distributed Pipelines for Investigating the Landscape of Natural Selection from Large-scale Genomic Datasets

    Organisational unit: Department of Evolutionary Anthropology

    Abstract:

    This project developed three Snakemake pipelines for detecting balancing selection, positive selection, and inferring the distribution of fitness effects. Azure Batch was tested for cloud deployment, and the first pipeline was successfully implemented in the cloud. The remaining pipelines are ready for deployment using insights gained from the first pipeline’s testing. Key results include contributions to three studies, showcasing the pipelines' effectiveness in analyzing genomes and exploring genetic diversity. Despite challenges with inadequate documentation for integrating Snakemake with Azure Batch, the project goals were partially achieved, with development carried out conservatively on local servers due to the novelty of cloud integration. Future work will focus on fully deploying all pipelines in the cloud and expanding their applications for large-scale genomic analyses.

  • Dylan Paltra | MULTIREP – Multidimensional Representation: Enabling An Alternative Research Agenda on the Citizen-Politician Relationship

    Organisational unit: Department of Government

    Abstract:

    The “MULTIREP” project aims to enable an alternative approach to studying the citizen-politician relationship. It focuses primarily on how citizens conceptualize representation. A mixed-methods approach combines qualitative methods (focus groups and one-to-one interviews with citizens) and quantitative methods in five countries (ca. 2.000 respondents in each), focusing on natural language processing approaches. In a multinational and multilingual mass survey in five countries, including 10.000 participants, we want to improve the current survey methodology by analyzing respondents’ answers to open-ended questions using different machine-learning approaches. During the funding period, the project team was able to conduct the survey, collecting rich text data from representative samples of the public. The team used the Azure infrastructure to analyze the open-ended text answers preliminary by prompting large-language models. These results complement a theoretically induced coding scheme, which will be used later in the analysis. Besides the already established dimensions of representation, the team found that citizens conceptualize representation very much in formalistic terms. The team plans to continue the usage of Microsoft Azure to thoroughly analyse the open-ended text answers, making use of not only large-language models but also more established natural language processing approaches.

  • Miguel Angel Rios Gaona | Controlled Machine Translation with Large Language Models for the Technical Domain

    Organisational unit: Centre for Translation Studies

    Abstract:

    Large Language Models (LLMs) have shown promising results on machine translation for high resource language pairs and domains. However, in specialised domains (e.g. medical) LLMs have shown lower performance compared to standard neural machine translation models. The consistency in the machine translation of terminology is crucial for users, researchers, and translators in specialised domains. In this study, we compare the performance between baseline LLMs and instruction-tuned LLMs in the medical domain. In addition, we introduce terminology from specialised medical dictionaries into the instruction formatted datasets for fine-tuning LLMs. The instruction-tuned LLMs significantly outperform the baseline models with automatic metrics, and quality estimation. Moreover, the instruction-tuned LLMs produce fewer errors compared to the baseline based on automatic error annotation.

Timetable funding 2024

  • 01.11.– 31.12.2023: Application for funding
  • 01.–14.01.2024: Internal review of applications and possible queries
  • Ab 16.01.2024: Announcement of funded projects by e-mail
  • 17.01.–31.01.2024: Setup of Azure environments by the ZID, onboarding of users
  • 01.02.–31.07.2024: Implementation of the projects
  • 01.08.–30.09.2024: Submission of project reports

Contact

If you have any questions about funding, please use the Servicedesk form Anfrage zu Microsoft Azure (enquiry about Azure, in German).