Project staff member

WU Vienna University of Economics and Business

Organisation/Company WU Vienna University of Economics and Business Research Field Technology » Knowledge technology Researcher Profile First Stage Researcher (R1) Positions Master Positions Application Deadline 11 Nov 2026 - 23:59 (Europe/Vienna) Country Austria Type of Contract Temporary Job Status Part-time Hours Per Week 30 Offer Starting Date 1 Jan 2027 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Do you want to use data to understand why the energy transition is so hard, and how to accelerate it? We offer an environment where you can realise your full potential, at one of Europe's largest and most modern business and economics universities, on a campus where quality of work is also quality of life.

Institute for Data, Energy, and Sustainability

Part-time, 30 hours/week

Starting January 01, 2027, and limited until September 30, 2029

We are hiring a pre-doctoral researcher (project staff, 30 hours/week) at the Institute for Data, Energy, and Sustainability (IDEaS), led by Prof. Kavita Surana and supported by the BMIMI Endowed Professorship for Data-Driven Knowledge Generation: Climate Action. The position starts on January 1, 2027 and ends on September 30, 2029.

What to expect

You will join a research group that develops data-driven evidence for energy transitions and climate action, starting from the research question and then building the data needed to answer it. This position focuses on (i) why capital-intensive energy technologies and infrastructures, such as offshore wind, geothermal, hydrogen, carbon capture, utilisation and storage (CCUS), and energy grids, succeed or stall, and (ii) how technology, firms, investors, and policy shape those outcomes.

You will build novel datasets by designing pipelines that combine and harmonise sources, with a special emphasis on turning unstructured and scattered information into structured, validated data using NLP, large language models (LLMs), or other relevant methods. You will then analyse these data with empirical methods, such as econometrics and statistical or network analysis. You will work in a highly interdisciplinary, international environment, with dedicated mentoring and regular, constructive feedback to support your growth as a researcher.

As part of the institute's team, your responsibilities will include:

• Conducting research: You will contribute to the institute's research projects, build and maintain high-quality datasets and data pipelines, and publish in leading international peer-reviewed journals.
• Writing a dissertation: You will pursue a PhD at WU and devote one third of your working hours to your dissertation, which can complement your project work
• Supervision: You will co-supervise bachelor's and/or master's theses.
• Engaging with external stakeholders: You will work with the institute's partners in industry, government, and academia to co-develop and communicate research insights.
• Supporting organisational duties: You will take on organisational and administrative tasks for the institute.
• Supporting teaching: Independent teaching within this position is available to support your career path -- non-continuous teaching of max. 3 SWS is supported as part of an external teaching assignment outside working hours. Within working hours, you will support the institute's teaching, including preparing course materials and assisting with the organisation and delivery of courses at the bachelor's and/or master's level.

What you have to offer

• Academic degree: You have a master's degree (or equivalent) that qualifies you for enrollment in a doctoral programme at WU, in a quantitative or analytical field such as data science, computer science, information systems, statistics, mathematics, physics, economics, or engineering (especially with a focus on empirical or data analysis). An interdisciplinary degree in the social sciences, public policy, or management with substantial quantitative training also qualifies.
• Essential skills:
  • Building datasets: Demonstrated experience constructing datasets from unstructured or heterogeneous sources, and motivation to build novel datasets rather than only analyse existing ones.
  • Coding: Advanced proficiency in Python or R, including version control, shown through projects, a thesis, or code you can share.
  • Empirical analysis: Training in econometrics or statistics.
  • Interest in energy and climate: Genuine motivation to work on the energy transition, especially on capital-intensive energy technologies and infrastructure.
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