Project staff member (pre-doc)

Wirtschaftsuniversität Wien; Wien; On-site; EUR 29,000 - 35,000; Part time

Benefits offered by this job

Six weeks vacation

Flexible hours

Training opportunities

Employee benefits

Modern campus

Excellent transit

Job summary

Wirtschaftsuniversität Wien's Institute for Data, Energy, and Sustainability (IDEaS) seeks a pre‑doctoral researcher for 30 hours/week starting 1 January 2027, ending 30 September 2029. You will join a data-driven research group focusing on energy transitions and climate action.

The role combines dataset construction using NLP/LLMs, empirical analysis with econometrics, and mentoring while pursuing a PhD at WU. Collaboration with industry, government, and academia is encouraged.

Qualifications

• Master's degree (or equivalent) that qualifies you for enrollment in a doctoral program at WU, in a quantitative field such as data science, computer science, information systems, statistics, economics, or engineering.
• Experience building datasets from unstructured sources and motivation to create new datasets.
• Proficiency in Python or R with version control, demonstrated through projects or a thesis.

Responsibilities

• Conduct research contributing to IDEaS projects and publish in leading journals.
• Build and maintain high-quality datasets and data pipelines.
• Pursue a PhD at WU, dedicating one third of working hours to dissertation; co-supervise theses.

Skills

Dataset construction

Python/R

Econometrics/Statistics

Energy/Climate interest

Education

Master's degree

Tools

Python

R

NLP/LLMs

Job 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 realize 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, utilization 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 harmonize 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 analyze 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 organizational duties: You will take on organizational 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 organization 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 program 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 analyze 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.
• Experience in any of the following is a plus: web scraping and data collection, document and information extraction, data integration and entity resolution, NLP and large language models, knowledge graphs, network science or complex systems, and a background in energy technology innovation, climate tech, energy policy, climate finance, industrial policy, or energy economics. You do not need all of these. We care more about how you approach a data problem than about any specific method.
• Language skills: You have excellent skills of written and spoken English. Knowledge of German (or willingness to learn it) is a plus.
• Working style: You take ownership of your work and enjoy being part of a team where people contribute to each other's success. You set high standards for yourself, care as much about asking the right question as about applying the right method, and thrive in an interdisciplinary, collaborative environment where you engage with multiple audiences.

We particularly encourage women to apply, including those from analytical fields who are new to energy or climate topics but have a strong interest in them.

Please direct any questions to Prof. Kavita Surana at [e-mail].

What we offer you

• Six weeks of vacation/Extra vacation time
• Flexible working hours
• Training opportunities
• A wide range of benefits, from an in‑house medical officer to athletic activities and a meal allowance to a variety of employee discounts
• Meaningful work in an open‑minded, inclusive, and family‑friendly environment
• A modern campus with spectacular architecture in the heart of Vienna
• Inspiring campus lifewith over 2,400 employees in research, teaching, and administration and approximately 21,500 students
• Excellent accessibility by public transportation

The minimum monthly gross salary amounts to €2,877.08 (14 times per year). This salary may be adjusted based on job‑related prior work experience. In addition, we offer a wide range of attractive social benefits.

WU is committed to diversity and inclusion and actively encourages applications from people of all backgrounds. We particularly welcome applications from women. Not sure if your profile fully matches our requirements? In case of equal qualification, female candidates will be given preference. Applicants with disabilities will be supported throughout each stage of the recruitment process. Candidates' qualifications will be assessed in the context of their academic age.