PhD student for hybrid digital twins of building energy systems in Dübendorf

The PhD position will focus on the development of methodology for hybrid digital twins for energy performance of buildings that enables a scalability of the developed models and transferability between different construction characteristics and operating conditions. The ultimate goal will be to use the hybrid digital twins to optimize the energy system operation of buildings.

The developed methodology will be hybrid, combing deep learning algorithms with physics-based inductive bias. One of the essential characteristics of the developed methodology is that it will be modular, enabling to configure the models and adapt them to new designs and setups. The second essential characteristic will be that the methodology will enable to parametrize the developed models to new construction characteristics, new operating and environmental conditions.

We are looking for a PhD student with a strong analytical background, and an outstanding MSc degree in Engineering, Control, Computer Science, Physics, Applied Mathematics, or a related field. The ideal candidate is proficient in both deep learning and building energy systems. Professional command of English (both written and spoken) is mandatory. The successful candidate shows enthusiasm for conducting original research and strives for scientific excellence.

We are offering a multifaceted and challenging position in a modern research environment with excellent infrastructure. This position will be available as soon as possible or upon agreement; the planned project is three years.

We look forward to receiving your online application until 30 November 2022 including a letter of motivation, CV, diplomas with transcripts of all obtained degrees, one publication e.g. thesis or preferably a conference or journal publication (either a link or upload) and contact details of three referees.
In addition, we expect as additional document a brief research statement (one page) describing your project idea in the field of physics-informed geometric deep learning for hybrid digital twins for building energy systems, making connection to your experience in this area and the related work from the literature.