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

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Empa's Urban Energy Systems Laboratory (UESL) in collaboration with EPFL's Intelligent Maintenance and Operations Systems Laboratory (IMOS) seeks a postdoctoral researcher to develop tabular foundation models for building and district-scale energy systems. The project aims to create foundation models that can learn from heterogeneous tabular data across different energy systems, operating conditions, and downstream tasks, combining UESL's expertise in energy system modeling with IMOS's advanced machine learning and AI methods.

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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 particular focus on transferability across heterogeneous energy systems and datasets
Validate and benchmark 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 joint research activities between UESL and IMOS
Publish and present research perspectives and results
Contribute to research proposals and acquisition of competitive funding

Profil

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 evaluation and adaptation of pre-trained models, fine-tuning strategies, and development of new model architectures or learning approaches
Strong research experience in deep learning and foundation models, including pre-trained models, fine-tuning, transfer learning, and self-supervised learning
Experience with tabular foundation models or foundation models for structured data
Strong understanding of modern deep-learning architectures and training strategies
Experience designing and rigorously evaluating new machine-learning methods
Excellent Python programming skills
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

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

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Über den Job

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