This 12-month (calendar year) postdoc researcher will develop innovative algorithms and methods for the analysis of volumetric 3D x-ray CT image data sets (time series and replicate samples) in collaboration with Dr. Jennifer Clarke, Director, Quantitative Life Sciences Initiative (bigdata.unl.edu/).
The analyses will include the segmentation of plant roots from soil in high-resolution 3D CT images collected for agricultural research; further analyses will include the statistical association of derived plant-root traits with genotypic and other phenotypic data. Development of these methods will involve both software and algorithms from statistical AI/machine learning, computational modeling, and distributed computing applications applied to very large volumetric data sets. And we will have a LOT of fun! This position will be located in Lincoln, Nebraska.
Required Qualifications
Ph.D. in statistics, computer science, or a related discipline.
Experience in the development of novel statistical/analytical methods for 3D volumetric image analysis.
Evidence of previous relevant research publications.
Experience with Python, PyTorch, R, and Linux environments.
Excellent written and verbal communication skills.
Preferred Qualifications
Experience with X-ray CT imaging analysis and interdisciplinary life sciences research.
Experience working and developing with the Torchvision framework and libraries.
Familiarity with distributed and high-throughput computing, and the SLURM scheduler.
Knowledge of plant sciences and plant phenotyping.
Strong desire to learn new skills and systems, and willingness to adapt to change.
Review of applications will begin September 20, 2024 and continue until the position is filled or the search is closed. To view details of the position and create an application, go to employment.unl.edu, requisition F_240116. Click "Apply for this Job" and complete the faculty information form. Attach the following documents:
A letter of interest that describes your qualifications for the job, anticipated contributions, and your experience working in teams or groups and your anticipated contributions to creating inclusive environments in which every person and every interaction matters (2 page maximum).
Your curriculum vitae.
Contact information for three professional references.
As an EO/AA employer, the University of Nebraska considers qualified applicants for employment without regard to race, color, ethnicity, national origin, sex, pregnancy, sexual orientation, gender identity, religion, disability, age, genetic information, veteran status, marital status, and/or political affiliation. See www.unl.edu/equity/notice-nondiscrimination.