PhD Position Foundation AI Models for Distribution Systems Decision-Making

.jobdescription td { padding: 0 5px; } .jobdescription * { border: none !important; border-width: 0 !important; font-size: 12px !important; font-family: Arial, Helvetica, sans-serif !important; color: black !important; } .jobdescription h1 b, .jobdescription h2 b, .jobdescription h3 b, .jobdescription h4 b, .jobdescription h1, .jobdescription h2, .jobdescription h3, .jobdescription h4 { font-size: 14px !important; font-weight: bold; margin-bottom: 0.5em !important; } #search-wrapper{display:none;} /* TUDelft Sidebar style */ .jobColumnTwo .joblayouttoken { background: #e3e3e3; color: #011b3c; padding: 0 20px 5px !important; } .jobColumnTwo .joblayouttoken .joblayouttoken-label { color: #039fd8; font-weight: bold; } .jobColumnTwo .joblayouttoken.marginTopLarge { padding: 20px 20px 5px 20px !important; } .jobColumnTwo .joblayouttoken.marginBottomLarge { padding: 0 20px 20px 20px !important; } /* TU Delft full-width header image */ .topleft.verticallyscaled.backgroundimage.large-image-component { height: 0 !important; } div[role='img'] .topleft.scaled.large-image-component{ height: 0 !important; position: absolute; padding-top: 20% !important; background-position: center !important; top: 50px; } div.job{ position: relative; padding-top: 26% !important; } /* Fix voor niet werkende knop */ div.jobTitle{ z-index: 2; position: relative; margin-top: 5px; } PhD Position Foundation AI Models for Distribution Systems Decision-Making Job description The Delft University of Technology is hiring a doctoral candidate on the subject "Foundation AI models for distribution systems decision-making ". Foundation models have recently emerged as a new learning paradigm in AI. These models learn from large datasets through self-supervision and have proved to generalize across many applications. Successful examples of foundation models include now commercial large language models (LLMs), but their applications extend to time-series data, audio, and video. The potential of foundation AI models for power systems has already been recognized, with features such as the ability to generalize across multiple tasks (e.g., power flow calculations, congestion management, voltage control) with a single architecture and to learn from multimodal data sources highlighted as crucial for power systems applications. Nevertheless, scientific challenges still need to be addressed before such foundation AI models can be used in a loop for decision-making in distribution systems. This PhD position aims to address some of these scientific challenges, including how to properly incorporate power systems physics into the foundation AI model architecture, as well as how to develop an architecture that is invariant to the distribution system topology. This is a four-year doctoral appointment. You will be jointly supervised by Dr. Pedro P. Vergara (Associate Professor) and Prof. Peter Palensky (Chair IEPG). You will be a member of the section Intelligent Electrical Power Grids in the Faculty of Electrical Engineering, Mathematics, and Computer Science. The project will offer opportunities to collaborate with industrial partners but also with academics from other disciplines, as required (mathematics, operations research). Within the team, we strive to develop methods that are mathematically rigorous and have near-term application potential. We are strong supporters of open science (publishing, source code, data). You will also be expected to assist in teaching activities (student supervision, labs) related to your subject area. About the ESE Department The research in the Department of Electrical Sustainable Energy is inspired by the technical, scientific, and societal challenges originating from the transition towards a more sustainable society and focuses on four areas: DC Systems, Energy Conversion and Storage (DCE&S) Photovoltaic Materials and Devices (PVMD) High Voltage Technologies (HVT) Intelligent Electrical Power Grids (IEPG) T