PhD on Grid-edge optimal solutions
4 weken geleden
Job Description
The project focuses on synthetical data generation, AI-driven state estimation, stochastic modelling and reliability assessment, and grid-edge optimal solutions. These models will be combined with the AI-driven state estimations to enhance network observability and grid monitoring. Additionally, integration with the stochastic modelling and reliability assessment process will provide valuable insights into the impact of uncertainties on grid reliability. Finally, in conjunction with the developed edge intelligence, these advancements will enable optimal solutions for the electricity grids in the Netherlands and e.g. the rest of Europe, while maintaining user privacy.
The research results will be immediately utilized by Alliander for congestion estimation and flexibility procurement. To achieve this, it is part of this project that all the developed (AI-driven) models and algorithms are also implemented in production-ready open-source packages.
One of the four main research tracks (RTs) of AISO is as follows:
RT4: Grid-edge optimal solutions
Along with the new generation of smart meters and the emerging development of virtual grid concept, there is a clear trend for system operation to push intelligence towards the edge devices.
To this end, within this research track, the successful candidate will work on designing privacy-aware accurate, fast, and efficient semi/self-learning AI models that can deliver results comparable with cloud-based fully supervised AI models. More specifically, the research will include:
Designing self-learning edge AI models based on the privacy/security-by-design principle Tackling AI model generalization Optimization of distributed and centralized AI model design and load balancing Integration and validation of simulation-based solutions in the virtual grid environmentSee for the other 3 reseach tracks below:
PhD1 / RT1: Synthetical data generation using multivariate models.
PhD2 / RT2: AI-driven state estimation and prediction.
PhD3 / RT3: Stochastic modelling and reliability assessment
Job requirements
A MSc degree in Computer Science, Data Science, or related fields A strong background in deep learning, distributed ML, and AI model optimization Good scientific programming skills and experience inlanguages such as Python, C++, Julia, etc. Enthusiasm in open-source and motivated to learn basic skills of scientific software engineering. Strong analytical, implementation, and experimentation skills Ability to work in an interdisciplinary team and be a team player Motivated to develop your teaching skills and coach MSc and BSc students Fluent in spoken and written English (C1 level)
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:
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. 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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