PhD in Artificial Intelligence for power load and renewable energy forecasting in electricity grids

Updated: 3 months ago
Job Type: Temporary
Deadline: 20 Oct 2022

Jheronimus Academy of Data Science (JADS) Den Bosch, is proud to start with three large Robust AI labs together with:

  • Deloitte (Auditing for Responsible AI Software Systems) – 5 PhD’s
  • DPG Media (responsible media lab) – 5 PhD’s
  • LaNubia  (Innovation Lab for Utilities on Sustainable Technology and Renewable Energy) – 5 PhD’s
  • Short Description 
    Are you an enthusiastic and ambitious researcher with a completed master's degree in a field related to machine learning (Computer science, AI, Data Science), or in Electrical Engineering with an affinity for AI and deep learning? Does the idea of working on real-world problem, and working with industry partners excite you? Are you passionate about using deep learning and explainable AI methods for power-load forecasting and achieving promising and renewable-energy forecasting? We are recruiting a PhD candidate who will develop accurate AI-powered electricity grid forecasting methods and trial them with industrial partners who work in the Dutch Caribbean islands.

    Job Description
    This position is linked to ILUSTRE, a new Innovation Center for Artificial Intelligence (ICAI) to be established in Curaçao. ILUSTRE will be a living lab in the Caribbean with the objective to develop, implement and test AI innovations that will accelerate the use of clean energy and advanced solutions in water treatment and wastewater recycling/purification. The innovation lab is one of the new ICAI labs that are part of the ROBUST program on Trustworthy AI-based Systems for Sustainable Growth which is financed under the NWO LTP funding scheme. ILUSTRE offers opportunities for PhD candidates who wish to support to the acceleration of the energy transition and who have an affinity with the Dutch Caribbean islands.

    Scientific Challenge
    Short-term forecasts of (a) power load and (b) renewable energy supply, are crucial for decarbonizing electricity grids: without these forecasts, high-carbon baseload generators must be kept running. The scientific challenge is to achieve accurate and reliable forecasts, in the face of changeable energy demand patterns and external covariates (weather, public events, etc). Deep learning has been shown to perform very well on power-load forecasting and achieves promising results in renewable-energy forecasting (Wang et al., 2019). This Ph.D. plan sets out to develop deep learning algorithms that realize forecasts that are both accurate and reliable, with the flexibility to adapt to local conditions. You will be contributing to the research conducted in ILLUSTRE by utilizing techniques such as (geometric) deep learning, and nonparametric (Gaussian process) regression.

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