Publisher
source

Florian Ziel

6 months ago

PhD and PostDoc Positions in Data Science for Energy Markets and Energy Systems University of Duisburg-Essen in Germany

Degree Level

PhD, Postdoc

Field of study

Computer Science

Funding

University employment under EG 13 TV-L for 3 years. PhD positions are 75% (29 h 52 min) and the PostDoc position is 100% (39 h 50 min), with part-time possible. The post mentions opportunities for further scientific qualification, close supervision, publication-oriented work, and involvement in public-funded and industry projects.

Deadline

Expired

Country flag

Country

Germany

University

Universität Duisburg-Essen

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Keywords

Computer Science
Data Science
Machine Learning
Electrical Engineering
Mathematics
Artificial Intelligence
Time Series Analysis
Python Programming
Uncertainty Analysis
Probabilistic Forecasting
Energy Sector
Econometric
Economics
Statistics
energy storage systems

About this position

University of Duisburg-Essen (Campus Essen) is advertising 3 PhD positions and 1 PostDoc position in the area of Data Science in Energy & Environment within the House of Energy, Climate & Finance (HECF).

The positions are centered on data science, statistics, econometrics, machine learning, and AI for energy markets and energy systems. Example research topics include probabilistic forecasting, uncertainty quantification, time series analysis, high-dimensional modeling, and data-driven models for electricity prices, renewables, flexibility, and system operations.

PhD applicants should have an above-average university degree in a quantitative field such as statistics/data science, mathematics, computer science, engineering, economics/business, or econometrics. The PostDoc role requires a PhD in a related quantitative discipline. Strong skills in probability, statistics, econometrics, or data science are expected, along with coding ability in Python, R, or C++. Experience with energy systems, markets, or environmental applications is desirable. Applicants should be fluent in English and willing to learn German.

The positions are 3 years long and paid under EG 13 TV-L. The PhD roles are advertised at 75% working time, while the PostDoc role is 100% (part-time possible). The post highlights close supervision, a publication-oriented environment, participation in seminars/workshops/conferences, collaboration in public-funded and industry projects, and flexible work options including home office.

Deadline: 2026-04-29. Start of employment is as soon as possible, with the LinkedIn post indicating a start window of Jun-Nov 2026.

Applications should be submitted with the usual documents, especially a motivation letter/statement, CV, and transcript of records, quoting the relevant reference number. The post provides separate application links for the PhD and PostDoc openings.

Funding details

University employment under EG 13 TV-L for 3 years. PhD positions are 75% (29 h 52 min) and the PostDoc position is 100% (39 h 50 min), with part-time possible. The post mentions opportunities for further scientific qualification, close supervision, publication-oriented work, and involvement in public-funded and industry projects.

What's required

Applicants should have an above-average university degree (at least 8 semesters standard period) in statistics/data science, mathematics, computer science, engineering, economics/business, econometrics, or a similar quantitative field. For the PostDoc position, a doctoral degree (PhD) is required. Candidates should have a strong background in probability, statistics, econometrics, or data science; knowledge of Python, R, or C++; interest in energy or environmental applications; interdisciplinary mindset; and fluent English. Experience with time series, forecasting, energy systems and markets, or environmental applications is desirable. German is not required initially but willingness to learn it is expected.

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Professors

Professor (full)

University Name
Country flag

Germany

Research Interests

Microeconomics

Behavioral Economics

Forecasting

Demand Forecasting

Econometric