Ph.D. Candidate (m/f/d) - Energy & Mobility Systems Scientist – Electric Truck Fleets, Smart Ch[...]

Technische Universität München (Technical University of Munich)

München

Vor Ort

EUR 48.000 - 62.000

Vollzeit

14 Tage+
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Zusammenfassung

Technische Universität München (Technical University of Munich) invites applications for a Doctorate in the Smart Mobility Lab. The project CEEDS4CI explores data exchange between fleets, charging infrastructure, and grid operators, focusing on heavy electric commercial vehicles.

You will model charging processes and fleet operations, forecast flexibility potentials from real data, and develop grid-friendly charging strategies, including bidirectional charging and coordination with partners,

Qualifikationen

  • Master's degree in a technical discipline with very good grades.
  • Strong knowledge of electrical energy systems and charging tech.
  • Solid programming skills, especially Python.
  • Good German and English language skills.

Aufgaben

  • Model charging processes and fleet operations for grid-friendly deployment.
  • Forecast flexibility potentials from real data and derive charging strategies.
  • Define interfaces and data flows between fleets, charging infra, grid operators, and energy management systems.
  • Coordinate work packages with partners and contribute to publications and open-source results.
  • Represent the institute in European collaborations and demonstrate results in lab and real charging operations.

Kenntnisse

Python programming
Time-series analysis
Optimization
Machine learning
Energy system modelling
German language proficiency
English language proficiency

Ausbildung

Master's degree in electrical engineering and information technology or related field

Jobbeschreibung

Academic staff

12.08.2026, Academic staff

Motivation

Two percent of the vehicles on Europe's roads, yet 27 percent of road transport's CO₂ emissions: few levers of the mobility transition are as powerful as heavy road freight. That heavy-duty transport must be electrified is settled. What remains open is how the energy system will cope. Charging parks for electric trucks require grid connections in the megawatt range - and at the same time, their fleets are one of the most valuable flexibility resources in the distribution grid. When a truck is connected to the grid, and for how long, is determined by fleet operations - but this knowledge never reaches the grid, because data between fleets, charging infrastructure, and grid operation flows through proprietary interfaces. This is exactly where the research project CEEDS4CI (Common European Energy Data Space for eMobility Charging Infrastructure) comes in: together with partners from research and industry, we are developing the interfaces through which fleets, charging infrastructure, and grid operators will exchange data in the future. At the Smart Mobility Lab, we view mobility as a system: we work with real operational data and model the interplay between the actors. Join the Smart Mobility Lab at the Institute of Automotive Technology and actively shape the future.

Your Challenge

We are looking for innovative applicants with initiative and drive who want to advance the grid-friendly integration of heavy electric commercial vehicles through their doctorate. At the center of the question of how much flexibility an electric heavy-duty fleet can actually provide to the energy system. You will model charging processes and fleet operations, forecast flexibility potentials from real operational and charging data, and derive charging strategies that can be used in a grid-friendly and economically viable way - up to bidirectional charging and marketing via virtual power plants. To this end, you will describe the relevant actors, data flows, and technical requirements between fleet operators, charging infrastructure operators, grid operators, and energy management systems; on this basis, you will specify the framework's optimization and coordination services and coordinate their implementation with our partners. You will then bring vehicles and charging infrastructure into the interoperability test and demonstrate their functionality in the laboratory and in real charging operations. In addition, you will represent the institute in a European consortium, take on responsibility in the coordinative leadership of work packages, and ensure the scientific and technical exploitation of results through publications, open-source contributions, standardization processes, and presentations at conferences and in expert networks.

Your Profile

As the ideal candidate, you hold a very good (≤1.9, German grading scale) master's degree in electrical engineering and information technology, energy engineering, automotive engineering, computer science, mechanical engineering, industrial engineering, or a comparable technical-scientific discipline. You bring solid knowledge of electrical energy systems and are familiar with battery and charging technology, charging infrastructure, bidirectional charging, and integration into distribution grids. Solid programming skills, especially in Python, are required; knowledge of time-series analysis, optimization, machine learning, or energy system modelling is an advantage. Above all, you bring initiative, analytical thinking, and enthusiasm for the electrification of heavy-duty transport. Business-fluent German and very good English skills are required.

Your Opportunities

As part of our team, you will be responsible for the entire chain from use-case definition through modeling to real-world test runs, moving between vehicle and charging technology, data analysis, and the energy economy. Rather than being limited to a single sub-aspect, you will independently shape the institute's technical contribution. Your work builds on the institute's prior work from the NEFTON and SPIRIT-E projects on heavy-duty electrification, vehicle-to-grid, and flexibility forecasting.

You will have access to extensive datasets and an interdisciplinary network of research, industry, and public-sector actors. As part of your doctorate, you will

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