Postdoctoral position in machine learning for experimental fluid mechanics (a) in Dübendorf

The postdoctoral researcher will work on the development of novel machine-learning methods to analyze experimental data obtained from large- and small-scale laboratory measurements. The initial focus will be on convolutional neural networks, autoencoders for model reduction, and automation for application to classic velocimetry techniques (PTV and PIV, i.e., particle-tracking and particle-imaging velocimetry, respectively), as well as background oriented Schlieren measurements. The postdoctoral researcher is also expected to participate and support other activities of the laboratory that are related to machine learning and artificial intelligence.

The applicants must hold a recent PhD degree, preferably in physics, data-science, computer science, mechanical engineering, or closely related fields. The ideal candidate has proven experience in working with real data and has documented track record of research in the field of machine learning and artificial intelligence. She/he is hands-on, skilled in programming (e.g., in Python), and with a deep knowledge of ML libraries. A deep knowledge of fluid dynamics, experience with velocimetry measurements and optical methods are an asset.

We are looking forward to receiving your application comprising a letter of motivation, a brief statement of your research interest and ideas for the offered position (please provide it in the same PDF as the motivation letter), your curriculum vitae, a complete list of publications, and two or more references with contact details.