Scientist position in data-driven modeling and machine learning with application to building physics, built environment and energy systems
Dübendorf, CH
vor 5 Tg.

We primarily welcome applicants with past postdoctoral experience to be hired at scientist level; however, younger applicants with an interesting profile may also be considered for hiring at post-doctoral level.

We are seeking a curious, creative, critical individual committed to scientific excellence and high ethical standard. The candidate has a background in physics, applied mathematics, mechanical / civil engineering, computer science, or related fields, and blends theoretical knowledge, programming skills and physical intuition.

The ideal candidate has worked in data science, machine learning and statistical learning, mastering the concepts behind the methods as well as the details of modern programming tools.

She / he has acquired experience in analyzing, describing and interpreting experimental data. Previous knowledge of building, energy, or environmental related problems is an asset.

Experience in developing, acquiring and managing research projects is also desirable for candidates to be hired at scientist level.

  • Possible research areas of interest include (but are not limited to) : developing ad-hoc solutions for construction, renovation and energy networks;
  • devising diagnostic and managing tools to optimize building and network performance; developing next-generation simulation tools that integrate mechanistic and datadriven models;
  • digitalization, smart monitoring and optimization of the information extracted from experimental data.

    We offer internationally competitive conditions, optimal computational and experimental facilities, the possibility to collaborate with several research groups at Empa, in Switzerland and abroad, and the opportunity to participate and contribute to the development of a research laboratory working on problems relevant for the future of our society.

    The expected starting date is September or at a later date upon mutual agreement. The position is initially offered for two years with the possibility to be extended in case of reciprocal satisfaction.

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