Leuven | More than two weeks ago
Two-dimensional transition metal dichalcogenides such as MoS2 have gained significant interest in the past decade for applications in future generation nanoelectronics, particularly as channel material in transistors, and optoelectronic devices. Electrical and optical properties of these devices are highly affected by the presence of defects at atomic scale. Scanning Transmission Electron Microscopy (STEM) based techniques are the most suitable to provide direct information on crystal structure and local defects. One of the main challenges with these techniques is the interpretation of the image. A procedure for automatically recognizing and quantifying defects in these materials starting from STEM images is needed.
You can contribute to this challenge by applying image processing and/or machine learning aided data analysis helping to develop image processing methodology to
Requirements Python and/or MATLAB programming needed, basic knowledge in image processing and machine learning desirable, familiarity with transmission electron microscopy and crystallography a plus
Type of project: Combination of internship and thesis, Thesis
Duration: 6 months
Required degree: Master of Engineering Technology, Master of Science, Master of Engineering Science
Required background: Nanoscience & Nanotechnology, Physics, Materials Engineering, Chemistry/Chemical Engineering, Computer Science
Only for self-supporting students.