Research Assistant @ Computer Vision Laboratory
Zürich Area, Switzerland
• Developing accurate and efficient algorithms to infer quantitative information from image data characterised by a low signal-to-noise ratio. • Focusing on image data acquired using fluorescence microscopy.Tutoring students in the lectures "Image analysis and computer vision" (Prof. Luc Van Gool and Prof. Gábor Székely) and in the sessions dedicated to image denoising during the EXCITE Summer School on Biomedical Imaging hold by ETH Zurich.Theoretical skills: physical-based models, Bayesian framework, hidden Markov models, Monte Carlo methods, variational image processing, convex optimisation.Practical skills: big data analytics, high performance computing, data base management, software engineering, a lot of coding in Python, some coding in R, Java and C++.