Master Student for the project AI4EFin

23 Dec 2025
Job Information

Organisation/Company

Bucharest Universty of Economic Studies

Research Field

Economics

Researcher Profile

First Stage Researcher (R1)

Positions

Master Positions

Country

Romania

Application Deadline

12 Jan 2026 - 16:00 (Europe/Bucharest)

Type of Contract

Temporary

Job Status

Part-time

Hours Per Week

10

Offer Starting Date

20 Jan 2026

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

At the Bucharest University of Economic Studies, the position of Master Students with 50% of the regular working time are to be filled as soon as possible, for the project AI4EFin, principal investigator Prof. Dr. Stefan Lessmann. The position is limited to 6 months, until June 30, 2026.

 

Applicants with a mathematical-quantitative profile are particularly welcome, even without a direct connection to banking and finance, if they are interested. In principle, you should have a university degree at master level in the field of economics, (business) mathematics, (business) informatics, statistics or similar with above-average success. Basic statistics, IT and programming skills as well as experience in empirical work are also helpful. Creativity, willingness to learn, scientific-oriented thinking as well as high communication and team skills should be a matter of course.

 

As a member of our team, you will deal with challenging questions of energy finance. Within the framework of your assignment, you will have the opportunity to present your results at international conferences. Our team offers flexible working hours and intensive cooperation in a committed team.

 

The application deadline is January 12, 2026 If you have any questions, please contact Prof. Daniel Traian Pele (danpele@ase.ro). You can find more details below, as well a short presentation of the project.

 

AI4EFin - presentation

 

Energy finance highlights the interdependency of energy and financial markets. While the traditional viewpoint of energy markets being a source for shocks in financial markets remains valid, the increasing financialization of energy products renders the linkage between those markets far more complex. Understanding these relationships and answering the crucial question of how to fuel world economies hunger for energy while decreasing greenhouse gas emission requires a new family of tools that turn the vast amounts of data in the energy finance ecosystem into insights for decision-making and ultimately enhance the efficiency, resilience, and sustainability of energy operations and their financing. 

 

The initiative AI for energy finance (AI4EFin) speaks to these challenges. Built around a methodological core, we craft novel machine learning (ML) and artificial intelligence (AI) instruments for pattern extraction, explanation, and forecasting of the high-dimensional, non-stationary, temporal data encountered in energy finance. 

 

We design this new family of ML/AI instruments to provide distinct features that support decision analysis and risk management in energy finance. These features include probabilistic models, which estimate the full conditional distribution of energy derivative prices and other targets. Distributional forecasts facilitate the applicability of risk management tools such as (conditional) value-at-risk and, thus, effectively support the quantification and management of financial and energy risks. 

 

Drawing on the potential outcome framework, recent work on transfer learning in transformer networks, we also devise ML/AI instruments that model the causal effect of interventions/shocks on price developments and market outcomes. Beyond their merit for risk management, these new causal approaches also guide policymakers in devising/revising regulatory programs and other market interventions, and facilitate estimating the effectiveness of these interventions.

Where to apply

Website

https://resurseumane.ase.ro/ai-for-energy-finance-ai4efin-760048-23-05-2023-cer…

Requirements

Research Field

Economics

Education Level

Bachelor Degree or equivalent

Skills/Qualifications

• A bachelor's degree in a relevant field such as economics, business administration, cybernetics and statistics, economic informatics, finance, statistics or similar.
• Solid knowledge of machine learning algorithms, statistical modeling, and data analysis techniques.
• intermediate level in programming languages such as Python (prefered) or R.
• Good understanding of energy markets and financial concepts.
• Good problem-solving skills and attention to detail.

Ability to collaborate effectively with researchers and analysts from different backgrounds

Specific Requirements

• Collect, preprocess, and analyze vast amounts of data from the energy finance ecosystem.
• Apply machine learning and statistical techniques to extract patterns and understand information.
• Develop and implement data-driven models for forecasting energy derivative prices and other relevant variables.
• Collaborate with the research team to design and refine ML/AI instruments for energy finance analysis.
• Publish research findings in reputable academic journals and present at conferences/ workshops.

Contribute to quantinar.com and the social media strategy of the research project. 

Languages

ENGLISH

Level

Excellent

Research Field

Economics

Additional Information
Benefits

Work in a dynamic group. 

Eligibility criteria

Good command of English. Knowledge in project field.

Selection process

Please see https://resurseumane.ase.ro/ai-for-energy-finance-ai4efin-760048-23-05-…

Website for additional job details

https://resurseumane.ase.ro/ai-for-energy-finance-ai4efin-760048-23-05-2023-cer…

Work Location(s)

Number of offers available

1

Company/Institute

Bucharest University of Economic Studies

Country

Romania

State/Province

Bucharest

City

Bucharest

Street

Piata Romana no 6

Geofield

Contact

City

Bucharest

Website

https://resurseumane.ase.ro/ai-for-energy-finance-ai4efin-760048-23-05-2023-cerere-de-finantare-162-15-11-2022/

Street

Piata Romana nr.6 sect.1

E-Mail

danpele@ase.ro

STATUS: EXPIRED

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