Publisher
source

Empa

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

Degree Level

Postdoc

Field of study

Computer Science

Funding

Postdoctoral research position at Empa with collaboration with EPFL. The post does not state stipend or salary details, but it offers a research appointment with opportunities for personal and professional development and the possibility of starting immediately or by agreement.

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Country

Switzerland

University

Empa

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Keywords

Computer Science
Environmental Science
Mechanical Engineering
Electrical Engineering
Deep Learning
Mathematics
Transfer Learning
Uncertainty Analysis
Optimisation
Surrogate Modeling
Statistics
ML

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About this position

Empa in Switzerland is advertising a Postdoctoral Researcher position in tabular foundation models for building and district energy systems. The project sits at the intersection of computer science, machine learning, electrical engineering, mechanical engineering, mathematics, and energy-system modelling.

The postdoctoral researcher will work at Empa’s Urban Energy Systems Laboratory (UESL) in collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL, led by Prof. Olga Fink. The research aims to develop, adapt, and benchmark 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.

Key research topics include evaluating existing pre-trained tabular foundation models, fine-tuning them for energy applications, developing new model approaches when needed, and validating models using building measurements, physics-based simulations, and energy-system optimization models. The work also includes applications such as prediction, surrogate modelling, uncertainty quantification, and decision support.

The ideal candidate has a PhD in a relevant field and strong experience in deep learning, foundation models, transfer learning, self-supervised learning, and Python-based research code. Experience with tabular or heterogeneous data, energy-system modelling and optimization, mixed-integer linear programming, or physics-informed machine learning is considered an advantage. Excellent English communication skills are required.

This is a postdoctoral opening, not a scholarship. The position offers an interdisciplinary research environment, close collaboration between Empa and EPFL, and the opportunity to shape an emerging research direction at the interface of artificial intelligence and energy-system optimization. The post indicates that the position can start immediately or by agreement.

Applications should be submitted through the online Refline portal linked in the post.

Funding details

Postdoctoral research position at Empa with collaboration with EPFL. The post does not state stipend or salary details, but it offers a research appointment with opportunities for personal and professional development and the possibility of starting immediately or by agreement.

What's required

Applicants should have a PhD in mathematics, electrical engineering, mechanical engineering, computer science, or a related field, with a strong methodological background in machine learning. Strong research experience with foundation models is expected, including evaluation and adaptation of pre-trained models, fine-tuning strategies, transfer learning, or self-supervised learning. Experience with tabular foundation models or structured data is particularly relevant. Strong Python programming skills, a strong track record in machine learning or a closely related field, and excellent written and spoken English are required. Preferred experience includes energy system modelling and optimization, tabular or heterogeneous data across multiple datasets/domains/tasks, mathematical optimization such as mixed-integer linear programming, uncertainty quantification, surrogate modelling, physics-informed machine learning, and understanding of energy-transition challenges.

How to apply

Apply via the Refline application portal using the provided link. Review the position details and submit your application materials through the online system.

More information can be found here

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