[Ieee_vis_open_positions] Postdoctoral Position in Computer Vision Analysis of Growing Plants

[Nelson Max]

We have a Postdoctoral scholar position in the Department of Computer 
Science at the University of California, Davis CA, USA, for a researcher 
in computer vision, image processing, image segmentation, or shape 
matching, to work on a project to understand the growth of tomato 
plants. The work includes multi-view stereo reconstruction, registration 
and joint analysis of multi-view stereo, thermal, and "pushbroom" 
multispectral imagery of growing plants, feature tracking in Micro 
Computed Tomography images of growing plant meristems, and developing 
models of plant growth, as well as helping to mentor two graduate 
students doing this research. The position is for 12 months, with a 
possible extension for up to 3 more months, and could start anytime in 
the calendar year 2013. Appointment as a postdoctoral scholar requires a 
doctoral degree (Ph.D., M.D.) or foreign equivalent (in this case, in 
Computer Science or a related filed such as Electrical Engineering or 
Mathematics). To apply, e-mail a CV to Nelson Max <max at cs.ucdavis.edu>. 
The abstract of our funded project is included below.

"In the near future, population increases combined with climate change 
are expected to place unprecedented demands on agriculture. Droughts are 
predicted to become more prevalent, nitrogen and phosphorous will become 
limiting, and saline environments may be accessed as arable land becomes 
depleted. Developing crop varieties to cope with such stresses under 
unpredictable climate conditions will require a nuanced understanding of 
genetic responses to environmental changes. Additionally, valuable water 
and fertilizer must be efficiently triaged to those plants facing the 
greatest deficit of resources. In this project, we will study responses 
to drought, salinity, and nitrogen and phosphorous deprivation in 
tomato, the second most valuable vegetable crop in California and 
worldwide. We will use RNA expression profiling to identify those genes 
most responsive to environmental stresses not only in domesticated 
tomato, but also its wild relatives, which may harbor sensitized 
responses to environmental change. We will develop high throughput 
methods to measure biochemical markers of stress, including remote 
multi-spectral sensing, thermal imaging, and stereo reconstruction. 
Additionally, we will analyze changes in the development and morphology 
of organs using Micro Computed Tomography to image the meristem and 
observe changes in leaves from their inception. We will direct our 
understanding of stress response towards the creation of genetically 
engineered tomato varieties that, from the outset of specific stresses, 
will visibly express a reporter, changing the color or structure of the 
plant. Such sentinel plants will allow the application of water and 
fertilizer as needed, rather than broadcasting these resources on 
potentially wasteful schedules."