Last updated: 09/26/2024
Job description: A postdoctoral associate position is available in the Bartesaghi lab to work in the areas of image processing, computer vision, machine learning, and deep learning with an emphasis on applications in computational photography, low-light imaging and burst-mode imaging. The candidate will be responsible for the design and development of algorithms for image and data analysis that work under extremely low SNR conditions, like those encountered in low-exposure and burst-mode photography as well as in emerging medical imaging modalities. Successful candidates will be able to use and develop modern computer vision and machine learning techniques in order to solve challenging problems in image reconstruction, alignment and classification, including multi-frame image synchronization and advanced strategies for image denoising.
Position 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 or machine learning. Programming skills in C++ or python as well as basic knowledge in parallel computing is desirable.
Our team: The candidate will join a 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 medical imaging detectors, our group has dedicated access to high-performance CPU/GPU resources provided by Duke University’s Research Computing and to 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 two or three references using the online form.
Last updated: 09/26/2024
Job description: A postdoctoral associate position is available in the lab of Dr. Bartesaghi to work in the development of methods and algorithms for high-resolution cryo-electron tomography (cryo-ET) and sub-volume averaging (SVA). The candidate will be responsible for the design, development and testing of novel computational strategies for analyzing tomographic data from a variety of important biomedical targets, using low-dose tomographic tilt-series provided by our collaborators or collected at our Shared Materials and Instrumentation Facility (SMIF) at Duke University. Emphasis will be made in the development of high-throughput approaches for tilt-series alignment and reconstruction as well as methods for improving the resolution of structures determined by SVA using hybrid data analysis techniques.
Position requirements: Ideal candidates should hold a PhD in the area of engineering, computer science, biochemistry or a related area and have some experience in cryo-EM image analysis. Programming skills in C++ or python as well as basic knowledge in parallel computing is desirable.
Our team: The candidate will join a 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 amounts of data produced by modern direct electron detectors, our group has dedicated access to high-performance CPU/GPU computing resources provided by Duke University’s Research Computing and to adequate IT infrastructure to support the development of computational approaches for high-throughout structure determination using continuous-tilt cryo-ET and high-resolution SVA. We also have access to a state-of-the-art microscopy facility including an FEI Titan Krios TEM equipped with Falcon III and Gatan K3 detectors, a Volta phase plate and a BioQuantum K3 imaging filter.
How to apply: Candidates should submit a CV, brief statement of research experience and interests, and the name of two or three references using the online form.
Last updated: 09/26/2024
Description: Duke ECE offers highly motivated doctoral students opportunity to develop research skills in our uniquely interdisciplinary environment. Direct admission to a research group allows PhD students to engage immediately with Duke ECE faculty members, working to cultivate the learning, thinking and problem-solving abilities needed to adapt, to develop and to exercise responsible leadership through times of rapid change. PhD research is fully funded, and our students also receive conference and travel support. Duke offers dedicated career development to support our PhD students in their next steps, as well as PhD Plus, which prepares students for careers in research and technology development. Find out more about the Duke ECE graduate programs at https://ece.duke.edu/grad.
How to apply: Interested graduate students should apply through the Duke ECE Masters and PhD programs, indicating that they would like to have Alberto Bartesaghi as research supervisor.
Last updated: 09/26/2024
Description: Duke CS gives incoming students an opportunity to investigate a range of topics, research problems, and research groups before committing to an advisor in the first year. Funding from the department and Duke makes it possible to attend group meetings, seminars, classes and colloquia. Students may work on multiple problems simultaneously while finding the topic that will motivate them through their first project. Sharing this time of learning and investigation with others in the cohort helps create lasting collaborators. Find out more about the Duke CS graduate program at https://www.cs.duke.edu/graduate.
How to apply: Interested graduate students should apply through the Duke CS Masters and PhD programs, indicating that they would like to have Alberto Bartesaghi as research supervisor.