Computer Science-OIT Technology Trainer / IT Analyst Opportunity
Duke University's Department of Computer Science and the Office of Information Technology invite applications for a joint-hire Innovation Co-Lab Technology Trainer / IT Analyst to support the university's undergraduate education mission in the areas of information technology and computer science through enhancement of curricular and co-curricular activities.
Bruce Donald and researchers from his Duke lab start company that uses software to overcome drug resistance, fight cancer
Duke's Bruce Donald, James B. Duke professor of computer science and researchers from his lab launched Gavilán Biodesign, which uses software to overcome drug resistance and fight cancer. Their company was selected as a startup by IndieBio, a biotech startup accelerator.Read More
Susan Rodger Recipient of Brooks Teaching Award
In recognition of her teaching excellence, Professor of the Practice in Computer Science and Director of Undergraduate Studies Susan Rodger received the 2019 David and Janet Vaughn Brooks Award from Duke University’s Trinity College. Congratulations!Read More
Shah, Reif, and Dubey to Present Temporal DNA Barcode Research at FNANO19 Conference
Shalin Shah, John Reif, and Abhishek Dubey of Oak Ridge National Lab just published research on a new imaging technique in which tiny light-up DNA barcodes identify molecules by their twinkling. The team will present their work on April 15, 2019 at the 16th Foundations of Nanoscience Conference (FNANO19).Read More
Computer science master's program at Duke ranks 13th in the country
Placing 13th in a recent ranking, the master's program for computer science at Duke University offers a strong research focus with an award-winning 30-credit M.S. in Computer Science that prepares individuals for both research careers and doctoral study.Read More
Jun Yang Publishes "Data Management in Machine Learning Systems"
Jun Yang, Associate Chair and Duke Professor of Computer Science, recently published Data Management in Machine Learning Systems, with coauthors Matthias Boehm and Arun Kumar. This important book follows a data-centric view of ML systems and provides a comprehensive overview of data management in ML systems for the end-to-end data science or ML lifecycle.