- Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
- Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539).
- A year-end bonus of 8.3% and annual vacation pay of 8%.
- High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
- Especially for PhD students, TU/e also grants opportunities for personal development, such as offering every PhD student a series of courses that are part of the Proof program, and the possibility of attending courses at the Dutch Institute of Systems and Control (DISC) and international courses and graduate schools as an excellent addition to their scientific education.
- An excellent technical infrastructure, on-campus children's day care and sports facilities.
- An allowance for commuting, working from home and internet costs.
- A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.
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PhD on Optimal Congestion-aware Control and Design of Charging Energy Hubs - Eindhoven, Nederland - Eindhoven University of Technology
Beschrijving
Job description
Driven by regulation and an expected lower total cost of ownership, the number of electric trucks and vans will increase substantially in the coming years, resulting in an expected additional electricity demand of almost 15% compared to the current total consumption in the Netherlands by 2050. The current electricity grid and regulations are not prepared to accommodate the huge increase in electricity demand for electric vehicles and the increase in renewable energy generation, leading to grid balancing and capacity problems on the electric grid that may ultimately impede the electrification of mobility and logistics. This project is part of a multidisciplinary consortium aimed at accelerating the electrification of the logistics sector by developing technological innovations to enable the integration of the charging infrastructure, battery storage systems, and distributed renewable generation within the existing energy grid. In this context, the design and the coordination of these components is of paramount importance to achieve a robust and sustainable energy grid operation.
Within this project, we aim at developing a digital-twin for scenario-based analyses and optimal system design of energy charging hubs considering, e.g., logistical planning activities, charging profiles, local storage systems, flexible assets (electrolysers, battery systems), and grid capacity. Specifically, we foresee four major subtasks: 1) Specification of an agent-based scenario toolbox and its interfacing with the open system design toolbox; 2) Development of an agent-based modelling environment for scenario-based analysis; 3) Validation of the models against real-world user-design case studies; 4) Optimization algorithms for combined charging infrastructure design and fleet operation, given other (stochastic) exogenous inputs.
This position is part of the joint interdisciplinary Nationaal Groeifonds project Charging Energy Hubs between, among others, the Control Systems Technology section in the Department of Mechanical Engineering at TU/e, Firan, Maxem, Netherlands Organization for Scientific Research (TNO), and Scholt Energy. During the project, the candidate will have opportunities to mentor students at many levels and take part in international scientific events.
Job requirements
Talented, enthusiastic, and open-minded candidates with excellent analytical and communication skills are encouraged to apply. A MSc degree in Mechanical Engineering, Electrical Engineering, Computer Science, Cybernetics, or a related discipline is required, as well as a strong background in control engineering, programming, and system modelling and identification. Experience and interest in power grid systems, model predictive control, and/or numerical optimization are of advantage.
Conditions of employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:
