h3Postdoctoral Researcher in Tabular Foundation Models for Building and District Energy Systems /h3pEmpa - Swiss Federal Laboratories for Materials Science and Technology /ppDübendorf, Switzerland /ph3Your tasks /h3ulliEvaluate 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. /liliAdapt and fine-tune existing foundation models for energy-system applications, investigating efficient adaptation strategies and the use of domain-specific data and knowledge. /liliDevelop new tabular foundation-model approaches where existing pre-trained models are insufficient, with a particular focus on transferability across heterogeneous energy systems and datasets. /liliValidate and benchmark the developed models using building measurements, physics-based simulations and energy-system optimization models. /liliInvestigate how tabular foundation models can support energy-system modelling and optimization, including applications such as prediction, surrogate modelling, uncertainty quantification, and decision support. /liliCoordinate the joint research activities between UESL and IMOS. /liliPublish and present research perspectives and results. /liliContribute to research proposals and the acquisition of competitive funding. /li /ulh3Your profile /h3pWe 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. /ph3Key qualifications include: /h3ulliStrong research experience in deep learning and foundation models, including experience with pre-trained models, fine-tuning, transfer learning, or self-supervised learning. /liliExperience with tabular foundation models or foundation models for structured data is particularly relevant. /liliA strong understanding of modern deep-learning architectures and training strategies, and experience designing and rigorously evaluating new machine-learning methods. /liliExcellent Python programming skills and strong hands-on experience implementing, training, and evaluating deep-learning models and research codebases. /liliA strong track record in machine learning or closely related fields. /liliExcellent written and spoken English. /li /ulh3Ideally, the candidate also: /h3ulliHas experience in energy system modeling and optimization. /liliHas experience with tabular or heterogeneous data, particularly across multiple da-tasets, domains, or tasks. /liliIs familiar with mathematical optimization methods, such as mixed-integer linear programming. /liliHas experience with uncertainty quantification, surrogate modelling or physics-informed machine learning. /liliHas an understanding of the technical challenges associated with the energy transition. /li /ul #J-18808-Ljbffr
Postdoctoral Researcher in Tabular Foundation Models for Building and District Energy Systems Arbeitgeber: 1000scholars
Das Max-Planck-Institut für Geoanthropologie in Jena bietet eine herausragende Arbeitsumgebung für PhD-Forscher, die sich mit quantitativen, historischen und archäologischen Stadtforschungen beschäftigen. Mit einem starken Fokus auf interdisziplinäre Zusammenarbeit, flexiblen Arbeitszeiten und umfangreichen Weiterbildungsmöglichkeiten fördert das Institut nicht nur die persönliche und berufliche Entwicklung seiner Mitarbeiter, sondern auch ein inklusives und familienfreundliches Arbeitsklima. Die Möglichkeit, an innovativen Forschungsprojekten zu arbeiten und Teil eines globalen Netzwerks von Wissenschaftlern zu sein, macht diese Position besonders attraktiv für engagierte Forscher.
