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École Polytechnique Fédérale de Lausanne

Postdoc in Tabular Foundation Models for Building and District Energy Systems École Polytechnique Fédérale de Lausanne in Switzerland

Degree Level

Postdoc

Field of study

Computer Science

Funding

Postdoctoral position; funding details are not specified in the post. The role is a research collaboration between EPFL and EMPA, with an immediate start date or by agreement.

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Country

Switzerland

University

École Polytechnique Fédérale de Lausanne

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Keywords

Computer Science
Environmental Science
Mechanical Engineering
Electrical Engineering
Uncertainty Analysis
Surrogate Modeling
Statistics
Decision Support System
Physics
Renewable Energy Systems
ML

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About this position

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

This postdoctoral opportunity is a collaboration between the Urban Energy Systems Laboratory at EMPA and the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at École Polytechnique Fédérale de Lausanne (EPFL). The project sits at the intersection of computer science, electrical engineering, mechanical engineering, physics, and statistics, with a strong focus on machine learning for engineering systems.

The research aims to advance tabular foundation models that can transfer across heterogeneous energy systems and tasks with limited task-specific data. Key topics include efficient adaptation of pretrained models, incorporating physical knowledge into foundation-model architectures and learning objectives, and generalization across datasets with different features, operating conditions, and target quantities. The testbed is building and district energy systems, with applications in prediction, surrogate modeling, uncertainty quantification, and decision support.

The post is especially suitable for candidates with a PhD and a strong publication or research record in machine learning, particularly in foundation-model architectures, pretraining, and efficient adaptation. Experience with tabular foundation models or heterogeneous structured data is highly relevant. Interest in engineering applications is essential, and prior exposure to energy systems is welcome.

The position starts immediately or by agreement. Applicants are asked to apply via the online portal and include a motivation letter, CV, a one-page research proposal, certificates and diplomas, and contact details for two referees.

Funding details are not specified in the post. The opportunity is based in Switzerland.

Funding details

Postdoctoral position; funding details are not specified in the post. The role is a research collaboration between EPFL and EMPA, with an immediate start date or by agreement.

What's required

Applicants should have a PhD and a strong research track record in machine learning, especially in foundation-model architectures, pretraining, and efficient adaptation. Experience with tabular foundation models or learning from heterogeneous structured data is especially relevant. Interest in foundation models for engineering applications is essential; prior experience in energy systems is welcome.

How to apply

Apply through the online portal linked in the post. Submit a motivation letter, CV, one-page research proposal, certificates and diplomas, and contact details for two referees.

More information can be found here

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