Project Description: 

Our lab is studying how daily temperature cycles interact with the circadian clock to regulate sunflower reproductive development. Specifically, we studying the daily timing with which individual florets open to present pollen and receptive stigmas at reproductive maturity. To do so, we have been growing sunflower diversity panels to map genetic variants that alter the timing of these events during summer field seasons at Davis. The time-lapse video data we collect captures not only these sunflower traits but also captures pollinator visitation. We would very much like to score the when, which, and how many pollinators are visiting in our large image datasets so that we can also determine what sunflower genes lead to variation those parameters, but our lab lacks strong expertise in this type of analysis. Therefore, we are looking for an undergraduate with experience in image analysis or machine learning to help us advance this project.

Department: 
PMB
Undergraduate's Role: 

The undergraduate will work with their mentor(s) in the lab to become familiar with the image data our lab has been collecting. Then, they will develop a training library and machine learning or other image analysis algorithms to score pollinator visitation in the images.

Undergraduate's Qualifications: 

The undergraduate should have experience with completing and leading computational projects. Experience with software for automated image analysis or machine learning would be particularly valuable. The student will be encouraged to attend summer lab meetings and may participate in data collection in the field if they are interested in that aspect of the project as well.

Location: 
On Campus
Hours: 
To be negotiated
Project URL: 
nature.berkeley.edu/blackmanlab