Postdoc in Deep Learning for Physics-Informed Machine Learning, EPFL, Switzerland

Lausanne ,Switzerland

  • Posted Date: 10/24/2023
  • Expires on: 11/15/2023
  • Description:We are seeking a highly motivated and skilled Postdoctoral Researcher to join our dynamic research team. The successful applicant will play a pivotal role in advancing the field of deep learning as applied to time series data derived from complex infrastructure and industrial systems. This position offers a unique opportunity to lead cutting-edge research, contribute to the development of innovative solutions, engage in academic mentorship and interact with industrial stakeholders.Eligibility:
    Ph.D. in Computer Science, Machine Learning, Data Science, Signal Processing or a related field: The ideal candidate should possess a strong academic background and expertise in deep learning, particularly as applied to time series data analysis, with specific experience or interest in Graph Neural Networks (GNNs) and/or PI-ML.Research Experience: Proven experience in conducting independent cutting-edge research in the field of deep learning, including GNNs and PI-ML, with a track record of publications in reputable journals and top-tier machine learning conferences.Mentorship Skills: Strong interpersonal and communication skills, as well as the ability to effectively mentor and guide students Innovative Mindset: Independence, curiosity, and innovation are highly valued qualities in the successful applicant. We encourage creative thinking and the exploration of novel research directions. Application process:
    Interested candidates should submit the following application materials:
    • a letter of motivation,
    • a detailed CV including a list of publications and awards (if applicable)
    • a short research statement (1-2 pages) outlining the intended research proposal, making connection to your experience in this area and the related work from the literature,
    • scanned transcripts of all obtained degrees (in English) (Doctorate, Master’s degree, other degrees)
    • the names and email addresses for 2-3 individuals who can provide reference letters.

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