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

h3Postdoctoral Researcher in Tabular Foundation Models for Building and District Energy Systems /h3pEmpa
- Swiss Federal Laboratories for Materials Science and Technology /ppDübendorf, Switzerland /ph3Your tasks /h3ulliEvaluate 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. /liliValidate and benchmark the developed models using building measurements, physics-based simulations and energy-system optimization models. /liliInvestigate how tabular foundation models can support energy-system modelling and optimization, including applications such as prediction, surrogate modelling, uncertainty quantification, and decision support. /liliCoordinate the joint research activities between UESL and IMOS. /liliPublish and present research perspectives and results. /liliContribute to research proposals and the acquisition of competitive funding. /li /ulh3Your profile /h3pWe 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. The 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. /ph3Key qualifications include: /h3ulliStrong research experience in deep learning and foundation models, including experience with pre-trained models, fine-tuning, transfer learning, or self-supervised learning. /liliExperience with tabular foundation models or foundation models for structured data is particularly relevant. /liliA strong understanding of modern deep-learning architectures and training strategies, and experience designing and rigorously evaluating new machine-learning methods. /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 /ulh3Ideally, the candidate also: /h3ulliHas 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