Postdoctoral Researcher in Tabular Foundation Models for Building and District Energy Systems — Empa
- Ort
- Dübendorf
- Vertrag
- full-time
- Veröffentlicht
- vor 10 Tagen
Rollenüberblick
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.
- 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.
- 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.
Wichtige Anforderungen
- 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.
- A strong understanding of modern deep-learning architectures and training strategies, and experience designing and rigorously evaluating new machine-learning methods.
- 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 candidate also:
- Has experience in energy system modeling and optimization
- Has experience with tabular or heterogeneous data, particularly across multiple da-tasets, domains, or tasks
- Is familiar with mathematical optimization methods, such as mixed-integer linear programming
- Has experience with uncertainty quantification, surrogate modelling or physics-informed machine learning
- Has an understanding of the technical challenges associated with the energy transition Our offer
Bewerbungsprozess
- 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.
Kontakte
- Applications sent via email or post will not be considered.
Weitere Details
- 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.
- Experience with tabular foundation models or foundation models for structured data is particularly relevant to this position. Key qualifications include:
- 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 candidate also:
- Has an understanding of the technical challenges associated with the energy transition
Notizen und Originalinhalt
- Your tasks
- Your profile
- Experience with tabular foundation models or foundation models for structured data is particularly relevant to this position.
- Key qualifications include:
- 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 candidate also:
Fragen zu dieser Stellenanzeige
Welches Gehalt bietet Empa für diese Stelle?
Empa gibt CHF 73'500 - 111'500 brutto pro Jahr für diese Position in Dübendorf an. Dies ist das im Original-Inserat veröffentlichte Gehalt (oder, falls der Arbeitgeber keinen Betrag nennt, eine realistische Schätzung für Rolle und Branche) — der Rechner auf dieser Seite berechnet daraus Ihr tatsächliches Netto nach Grenzgänger-Steuer und Sozialabgaben.
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