Postdoctoral Researcher in Tabular Foundation Models for Building and District Energy Systems

ppMaterials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living. Empa is a research institution of the ETH Domain. /p h3Postdoctoral Researcher in Tabular Foundation Models for Building and District Energy Systems /h3 pOur passion lies in materials science and technology, and at Urban Energy Systems Laboratory (UESL), we develop strategies and methods to support the creation of decarbonized, resilient, and equitable energy systems. /p pThis PostDoc position is offered in collaboration with Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL (Prof. Olga Fink). IMOS develops advanced machine-learning and AI methods for complex engineering and industrial systems, with a particular focus on improving their reliability, availability, and operational performance while enabling more efficient and cost-effective maintenance. /p pTo advance the development of tabular foundation models for energy systems, we are seeking a highly motivated and skilled postdoctoral researcher. The project aims to develop foundation models that can learn from heterogeneous tabular data across buildings and district-scale energy systems and transfer across systems, operating conditions, and downstream tasks. The position combines Empa UESL’s expertise in developing and accessing energy system models with the methodological expertise of the IMOS Laboratory in machine learning and foundation models. /p h3Your tasks /h3 ulliEvaluate and benchmark existing pre-trained tabular foundation models for building- and district-scale energy applications, assessing their transferability and generalization across systems, operating conditions, and downstream tasks. /liliAdapt and fine-tune existing foundation models for energy-system applications, investigating efficient adaptation strategies and the use of domain-specific data and knowledge. /liliDevelop new tabular foundation-model approaches where existing pre-trained models are insufficient, with a particular focus on transferability across heterogeneous energy systems and datasets. /lilibValidate and benchmark the developed models /b using bbuilding measurements /b, physics-based simulations and energy-system optimization models. /lilibInvestigate how tabular foundation models can support energy-system modelling and optimization /b, including applications such as prediction, surrogate modelling, uncertainty quantification, and decision support. /lilibCoordinate /b the joint research activities between UESL and IMOS. /lilibPublish and present /b research perspectives and results. /liliContribute to bresearch proposals /b and the acquisition of competitive funding. /li /ul h3Your profile /h3 pWe seek a highly motivated and dedicated researcher with a PhD in mathematics, electrical or mechanical engineering, computer science or a related field, and a strong methodological background in machine learning. /p pThe ideal candidate has demonstrated research experience with foundation models, including the evaluation and adaptation of pre-trained models, fine-tuning strategies, and the development of new model architectures or learning approaches. Experience with tabular foundation models or foundation models for structured data is particularly relevant to this position. /p h3Key qualifications include: /h3 ulliStrong research experience in deep learning and foundation models, including experience with pre-trained models, fine-tuning, transfer learning, or self-supervised learning. Experience with tabular foundation models or foundation models for structured data is particularly relevant. /liliExcellent Python programming skills and strong hands-on experience implementing, training, and evaluating deep-learning models and research codebases. /liliA strong track record in machine learning or closely related fields /liliExcellent written and spoken English /li /ul h3Ideally, the candidate also: /h3 ulliHas experience in energy system modeling and optimization /liliHas experience with tabular or heterogeneous data, particularly across multiple da-tasets, domains, or tasks /liliIs familiar with mathematical optimization methods, such as mixed-integer linear programming /liliHas experience with uncertainty quantification, surrogate modelling or physics-informed machine learning /liliHas an understanding of the technical challenges associated with the energy transition /li /ul h3Our offer /h3 ulliA stimulating and interdisciplinary research environment and opportunities for personal and professional development /liliClose collaboration between Empa and EPFL, bringing together complementary expertise in energy systems, machine learning, and foundation models /liliThe opportunity to shape an emerging research direction at the interface of artificial intelligence, energy-system modelling and optimization /liliThe possibility of starting immediately or by agreement /li /ul pWe foster a culture of inclusion and respect. We welcome all people who are interested in innovative, sustainable and meaningful activities - that's what counts. /p /p #J-18808-Ljbffr