Postdoctoral Researcher in Tabular Foundation Models Building and District Energy Systems

Materials 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.

Our passion lies in materials science and technology, and at the Urban Energy Systems Laboratory (UESL), we develop strategies and methods to support the creation of decarbonized, resilient, and equitable energy systems.

This Post Doc position is offered in collaboration with the 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.

To 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.

• 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.

We 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.

Key qualifications include:

• Strong 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.
• Excellent Python programming skills and strong hands-on experience implementing, training, and evaluating deep-learning models and research codebases.
• A strong track record in machine learning or closely related fields
• Excellent written and spoken English

Ideally, the…