Description: A postdoctoral fellow and research assistant positions are available in the Bartesaghi Lab to work in the area of computational imaging with an emphasis on low-SNR and burst-mode imaging applications. The candidate will be responsible for the design and development of algorithms for image and data analysis that work under low SNR conditions, like those encountered in low-light and burst-mode photography and in emerging medical and molecular imaging modalities. Successful candidates will need to use and develop modern computer vision, machine and deep learning techniques in order to solve challenging problems in image reconstruction and alignment, including multi-frame image synchronization and collaborative image denoising.
Requirements: Ideal candidates should hold a PhD in the area of electrical engineering, computer science, applied mathematics or a related area and have experience in image processing, computer vision and machine learning. Programming skills in C++ or python as well as basic knowledge in parallel computing platforms is desirable.
Our team: The candidate will join a vibrant and highly dynamic team of researchers that span across different departments at Duke University, including Computer Science, Biochemistry, Electrical and Computer Engineering and others. The group emphasizes collaborative and multidisciplinary work bringing together expertise from various areas of science including mathematics, engineering and the life sciences with activities that encompass the College of Arts and Sciences, the Pratt School of Engineering, and the Duke University School of Medicine. In order to analyze the unprecedented volumes of data produced by modern devices such as mobile cameras and state-of-the-art molecular imaging detectors, our group has dedicated access to high-performance CPU/GPU resources provided by Duke University’s Research Computing and access to the adequate IT infrastructure to support the development of novel computational approaches for exploration and analysis of big data and high-throughput data processing.
How to apply: Candidates should submit a CV, brief statement of research experience and interests, and the name of three references using the link provided.
Tagged as: Computer Science, Engineering, Life Sciences
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