Research opportunities in computational optics
The Pegard lab (www.pegardlab.com) is looking for motivated students to contribute to several open research projects. The projects range from software development to deep learning in optics research. Some of the open projects include but are limited to:
1. Developing a software package similar to the infamous Keras deep learning library for optics applications. For this project, proficiency in Python is required and some familiarity with Keras is encouraged.
2. DeepCGH2.0, a deep learning-based algorithm for hologrpahic optogenetics. This research will focus on different types of deep learning models and will be deep learning heavy. Some familiarity with deep learning and machine learning techniques is required. This project would follow up a recent publication: https://opg.optica.org/oe/fulltext.cfm?uri=oe-28-18-26636&id=437573
3. Deep learning-based image quality assessment. The aim of this project is to advance the current state of the art in image quality assessment metrics. Some familiarity with deep learning is required.
The benefits of joining our research group: we prefer to have students in our lab for years and invest in their training but shorter stays are also welcome. You will be part of a very active and friendly group of scientists from different departments, giving you the opportunity to grow your network even beyond Pegard lab. Our research is cutting edge and your contributions will have both scientific and industrial impact with the potential for conference and journal publications. You will get the opportunity to present your contributions at regional and national conferences.
please inquire by emailing Nico, pegard@unc.edu, and Hoss, hebi@live.unc.edu with 1 – a CV, 2- a description of the project(s) that you would like to explore. Please let us know how many hours a week you are able to invest in research. We currently only consider volunteer research opportunities. Our lab has a track record of retaining motivated undergrads who show progress and commitment and can consider either research for credit, or paid positions in subsequent semesters.