Experienced Researcher for the project AI4EFin

11 Mar 2024
Job Information

Organisation/Company

Bucharest Universty of Economic Studies

Research Field

Economics

Researcher Profile

Established Researcher (R3)

Country

Romania

Application Deadline

18 Mar 2024 – 16:00 (Europe/Bucharest)

Type of Contract

Temporary

Job Status

Part-time

Hours Per Week

10

Offer Starting Date

1 Apr 2024

Is the job funded through the EU Research Framework Programme?

Not funded by an 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 a Experienced Researcher with 25% of the regular working time is to be filled as soon as possible, for the project AI4EFin, principal investigator Prof. Dr. Stefan Lessmann. The position is limited to 3 years.

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 PhD level in the field of economics, (business) mathematics, (business) informatics, statistics or similar with above-average success. 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 September 4, 2023. If you have any questions, please contact Prof. Daniel Traian Pele ([email protected]).  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.

Requirements

Research Field

Economics

Education Level

PhD or equivalent

Skills/Qualifications

• Ph.D. degree in economics, finance, public policy, or a related field.
• Solid understanding of energy markets, financial markets, and their interrelationships.
• Proficiency in econometric analysis and  modeling techniques.
• Working knowledge in causal machine learning
• Familiarity with risk management frameworks and decision analysis in energy finance.
• Strong analytical and quantitative skills.
• Ability to conduct independent research and produce high-quality reports and policy briefs.

Specific Requirements

• Apply causal machine learning techniques to assess the effect of interventions/shocks on price developments and market outcomes and provide recommendations for policy revisions.
• Collaborate with the research team to identify key policy questions and design research methodologies to address them.
• Conduct economic and statistical analysis of energy markets and financial data.
• Communicate research findings to stakeholders, policymakers, and industry professionals through reports, presentations, and policy briefs.
• 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

Years of Research Experience

4 – 10

Additional Information
Benefits

Work in a dynamic group.

Eligibility criteria

Good command of English. Knowledge in project field.

Selection process

Please see https://fondurieuropene.ase.ro/anunturi/

Website for additional job details

https://fondurieuropene.ase.ro/anunturi/

Work Location(s)

Number of offers available

1

Company/Institute

Bucharest Universty of Economic Studies

Country

Romania

State/Province

Bucharest

City

Bucharest

Street

Piata Romana no 8

Geofield

Where to apply

Website

https://fondurieuropene.ase.ro/anunturi/

Contact

City

Bucharest

Website

http://www.ase.ro

Street

Piata Romana nr.6 sect.1

E-Mail

[email protected]

STATUS: EXPIRED