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

Evaluate 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. Adapt and fine-tune existing foundation models for energy-system applications, investigating efficient adaptation strategies and the use of domain-specific data and knowledge. Develop new tabular foundation-model approaches where existing pre-trained models are insufficient, with a particular focus on transferability across heterogeneous energy systems and datasets. Validate and benchmark the developed models using building measurements, physics-based simulations and energy-system optimization models. Investigate how tabular foundation models can support energy-system modelling and optimization, including applications such as prediction, surrogate modelling, uncertainty quantification, and decision support. Coordinate the joint research activities between UESL and IMOS. Publish and present research perspectives and results. Contribute to research proposals and the acquisition of competitive funding.

Empa is a research institution of the ETH Domain. 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. At the Urban Energy Systems Laboratory (UESL), we develop strategies and methods to support the creation of decarbonized, resilient, and equitable energy systems. This PostDoc position is offered in collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL.

• PhD in mathematics, electrical or mechanical engineering, computer science or a related field

• Strong methodological background in machine learning

• 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

• Strong research experience in deep learning and foundation models, including experience with pre-trained models, fine-tuning, transfer learning, or self-supervised learning

• Strong understanding of modern deep-learning architectures and training strategies

• Experience designing and rigorously evaluating new machine-learning methods

• Excellent Python programming skills and strong hands-on experience implementing, training, and evaluating deep-learning models and research codebases

• Strong track record in machine learning or closely related fields

• Excellent written and spoken English

• Experience in energy system modeling and optimization (ideally)

• Experience with tabular or heterogeneous data, particularly across multiple datasets, domains, or tasks (ideally)

• Familiarity with mathematical optimization methods, such as mixed-integer linear programming (ideally)

• Experience with uncertainty quantification, surrogate modelling or physics-informed machine learning (ideally)

• Understanding of the technical challenges associated with the energy transition (ideally)