Postdoctoral Position in Computer Vision Analysis of Growing Plants at U. C. Davis, California, USA 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 <mailto:max@cs.ucdavis.edu> &lt;max(a)cs.ucdavis.edu&gt;du>. 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."