Postdoctoral Researcher in Deep Learning for Time Series Data Analysis in Complex Infrastructure and Industrial Systems with a particular focus on Graph Neural Networks (GNNs) or Physics-Informed Machine Learning (PI-ML)

Key Responsibilities:

Research Leadership: The primary responsibility of the postdoctoral researcher will be to lead and drive research projects focused on the development of novel deep learning techniques, including Graph Neural Networks (GNNs) and Physics-Informed Machine Learning (PI-ML) algorithms, in order to create innovative solutions for the intelligent maintenance and operation of complex infrastructure and industrial systems. This will involve conducting experiments, developing novel algorithms, and publishing research findings in reputable academic journals and top-tier machine learning conferences.

Mentorship: The position includes independent supervision of master students and involvement in the supervision of PhD students.

Teaching: While the primary focus is on research, there will be minimal teaching responsibilities associated with this position.

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.

Qualifications:
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.