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Postdoctoral Researcher in Tabular Foundation Models for Building and District Energy Systems – Dübendorf | JobRevolt

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Postdoctoral Researcher position in collaboration with Empa's Urban Energy Systems Laboratory (UESL) and EPFL's Intelligent Maintenance and Operations Systems Laboratory (IMOS, Prof. Olga Fink). 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.

Aufgaben

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

Profil

PhD in mathematics, electrical or mechanical engineering, computer science or a related field
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
Strong understanding of modern deep-learning architectures and training strategies, and experience designing and rigorously evaluating new machine-learning methods
Excellent 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
Ideally: 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

Rahmenbedingungen

Start: immediately or by agreement

Benefits

Stimulating and interdisciplinary research environment and opportunities for personal and professional development
Close collaboration between Empa and EPFL, bringing together complementary expertise in energy systems, machine learning, and foundation models
Opportunity to shape an emerging research direction at the interface of artificial intelligence, energy-system modelling and optimization
Culture of inclusion and respect welcoming all people interested in innovative, sustainable and meaningful activities

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

Über den Job

Über den Job

Aufgaben

Profil

Rahmenbedingungen

Benefits

Skills

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