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Jobs Posted on the Whova Community Board of 2022 NAPPN Annual Conference

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Scientific Programmer and Educator
University of Arizona
See link for more details

The Data Science Institute and the University of Arizona College of Agriculture and Life Sciences Data Science team have two openings for a scientific software engineer and educator. The ideal candidate is either a domain scientist with software and programming experience or a software engineer with experience applying computational and statistical methods to support scientific research.
The incumbents will split time between providing support on research projects and offering training across campus. With multiple openings, we are hoping to find two with complimentary experience and skills.

The education component of the position will develop, enhance and expand the Data Science Institute’s capabilities to develop curriculum and provide just-in-time training, workshops, consultation and support for students, faculty and staff wanting to learn foundational tools and concepts of data science and reproducible computational science. This will include broad collaboration across the data science community, including with other campus partners and trainers related to computational science and data science.

The research component of the position will directly support research projects across the College of Agriculture and Life Sciences, Cooperative Extension, and Experiment Station. They will be expected to conduct and communicate reproducible research, data management, and analysis.

Master’s degree required; PhD preferred
Experience in scientific software development,
Proficiency in R or Python.

Salary: 55-70k
Postdoctoral Associate - Miller Lab
Donald Danforth Plant Science Center
The Miller Lab at the Donald Danforth Plant Science Center (DDPSC), in collaboration with The Land Institute in Salina (KS) is currently seeking a highly motivated Post-Doctoral Associate to explore whether phenomic-derived predictors – phenomic estimated breeding values – can match or even outperform the prediction of elite individuals of perennial species compared to pedigree and genomic prediction. Key outcomes of this research will be to determine whether phenomic estimated breeding values can enhance or substitute for genomic selection and to build an efficient, effective, generalizable phenomic selection pipelines to expedite new crop development. This is a 12-month, 100% research position established for a one-year provisional period with annual renewal for up to four and a half years. We anticipate that the candidate will be based in St. Louis, MO or Salina, KS or within a two hour drive of these two locations. The Post-Doctoral Associate will be co-mentored by Allison MIller (Danforth Plant Science Center) and Brandon Schlautman (The Land Institute). To learn more about the Miller Lab, visit our webpage ( For more about The Land Institute, please see here:
Postdoctoral Research Associate
University of Illinois
The Environmental Plant Physiology Lab at the University of Illinois Urbana Champaign (UIUC) is recruiting a Postdoctoral Research Associate (full-time, 1-year duration, renewable upon performance and funding availability) to work on high-throughput crop phenotyping and modeling using proximal/remote sensing techniques. This position is funded by an international research project (the RIPE project) that is engineering crops to be more productive by improving photosynthesis. The Postdoctoral Research Associate will be jointly advised by Prof. Carl Bernacchi at UIUC/USDA-ARS and Prof. Peng Fu at Harrisburg University and ideally will be based in Champaign-Urbana, IL (location can be negotiated). The selected candidate will also have opportunities to work/collaborate with quite a diverse cohort of scientists in backgrounds such as plant physiology, remote sensing, process-based crop modeling, and cloud computing.
Specific responsibilities of this position include: developing and implementing data analytic pipelines to process proximal and/or remote sensing data (hyper-/multi-spectral, LiDAR, and thermal); building statistical and/or machine learning based predictive models to quantify plants traits, such as photosynthetic traits, evapotranspiration, water use efficiency, as associated with crop photosynthesis; integrating/fusing various sensor data for understanding crop yield performance; preparing peer-reviewed publications and project reports; and disseminating research at professional meetings. The selected candidate will be expected to work independently and as a team member to accomplish the research objectives.
Further details related to applications can be found in
Data Science Trainer
Donald Danforth Plant Science Center
The Data Science Trainer will work at the intersection of computational sciences, education, and entrepreneurship. The Data Science Trainer will work with members of the Data Science, Education Research and Outreach Laboratory (EROL), and Innovation teams to develop and implement a short-format training (e.g. short-course, workshop) program. The training program will provide ongoing skill development in a variety of computational skills including programming (Python in particular), version control, statistical analysis (R preferred), data visualization, data management, bioinformatics, image analysis, geospatial analysis, and other relevant skills. The training program’s target audiences include members of the Donald Danforth Plant Science Center and broader St. Louis innovation community.
Data and Modeling Lead
Running Tide
We’re looking for a Data and Modeling Lead to lead our Quantification team. This team of 2-3 people builds the computational system that integrates our own in situ data with fundamental ocean modeling. Quantification provides the feedback loop from experimentation into further innovation, while also being integral to the commercialization of our technology. Specific topics will include the modeling of nutrient and particle flows in ocean currents, as well as the dynamical characterization of biological growth. This mission critical role requires an energetic and organized analytical mind capable of balancing individual work while directing a small team. This is a full-time, salaried position with benefits.

More details about the job posting and what would make a good applicant.
Postdoctoral Associate - New Roots for Restoration Biology Integration Institute
Donald Danforth Plant Science Center
The Danforth Center is recruiting three postdoctoral associates to join the New Roots for Restoration Biology Integration Institute (NRR-BII), a 5-year, NSF-funded initiative supporting integrated research and training activities at the Center and NRR-BII partner institutions.

Research activities of the NRR-BII focus on the overarching theme of how plant organismal systems (plant roots and shoots) relate to one another, and how those relationships influence and are influenced by plant communities and the soil ecosphere.

Successful candidates will be co-mentored by at least two NRR-BII Danforth PIs (Miller, Baxter, Gehan, Topp, Fahlgren). Postdoctoral research associates will lead research projects and/or Institute Expertise Cores, and will contribute to ongoing research across the Institute. NRR-BII Institute intentionally sustains a culture of diversity, equity, and inclusion to support, train, and retain the next generation of diverse scientists. Postdoctoral research associates will play a key role in shaping this culture and mentoring Institute trainees.

About the Positions:
We are seeking 3 postdoctoral research associates, 1 in each of the general areas listed below.
Field data collection and analysis. Lead field-based research activities near St. Louis, MO. Apply root phenotyping approaches for diverse perennial species in the field and greenhouses; integrate root phenotyping with above-ground phenotypes and soil data; use genomic tools to investigate genetic basis of root-shoot covariation.

Data synthesis. Lead data synthesis including multi-dimensional temporal sequences of above- and below-ground plant phenotyping data, leaf elemental composition data, and soil data; help oversee one or more expertise cores.

Phenotyping analysis. Contribute to the development of above-ground in-field imaging via a new phenotyping app, data pipelines and analysis. Help implement app across projects.
Contact Baxter, Gehan or Fahlgren with Qs at NAPPN
Deep learning scientist
Deep learning scientist, more information in the link
Postdoctoral Associate of Predictive Analytics in Crop Breeding and Management
Cornell University
The position is in the Sections of Plant Breeding and Genetics (Gore Lab) and Soil and Crop Science (Sun Lab). The successful applicant is expected to integrate physiological and environmental processes with genetic data to understand and forecast plant performance at different spatial and temporal scales. A special focus of this position is on the development and application of crop growth models from remote sensing data to enable genetic dissection and prediction of plant phenotypes. Please see the full announcement at:

Position requirements
• Ph.D. with research experience in an ecology-, agriculture-, climate-, remote sensing-, or computation-related field.
• Proficiency in collecting and analyzing remote sensing datasets.
• Extensive skills in big-data analysis, data mining, and statistics.
• Programing experience in C, Matlab, R or Python.
• Experience with plant physiology and phenotyping technologies.
• Independence and project leadership skills.
• Demonstrated contribution to Diversity, Equity and Inclusion initiatives.
• Excellent interpersonal and communication skills with a strong publication record.
Preferred qualifications
• More than 3 years of experience in remote sensing, crop simulation models, or genetic analysis of genotype-by-environment interactions
• Proficiency in developing and implementing complex computational pipelines on Linux operating systems and high-performance computing clusters.
• A record of publication in the field of remote sensing and physiological modeling.
• Experience in the use of APSIM/DSSAT models.
How to Apply
Interested applicants should submit a cover letter, CV, statement of research interest, contact information for 3 references and a statement of contribution to diversity, equity, and inclusion via Academic Jobs Online at: The position is anticipated to start in May 2022 and the search will continue until filled.
Postdoctoral Fellow: AI-driven UAV image analysis
We are seeking a postdoctoral researcher to develop UAV image analysis methods with greater flexibility and automation for applications in diverse germplasm and species. This includes wild accessions, mutant populations, and heirloom varieties at both the plot and plant levels. Initial work will focus on maize diversity. Contact Jacob Washburn ( for more information. USDA-ARS is an equal opportunity employer.
Applied Image / Phenotyping / Data Research Associate
RD4AG (Research Designed for Agriculture)
RD4AG, a field research company in Yuma Arizona specializing in small plot trials is looking to develop its network of team members who are interested in working with us in our phenotyping / sensor / image / data evaluation programs.
Our programs are heavily image based, where we use them to evaluate plant canopy cover, plant diseases, insect levels, roots in rhizotrons, weed populations, growth stage and quality of harvested produce, etc etc etc. In the realm of sensors, we utilize NDVI, IR Radiometer, soil moisture, soil salinity, etc.
We deal with a broad range of crops. We envision a network of motivated and enthusiastic team members who can devote some time each month to various projects. It is perfect for a student needing some spending money for a few hours work, or an at home kid or parent care person desiring to stay involved, to a person looking for a steady part time job. This can be from a few hours a month or most weeks or a couple dozen hours in a block one a month or ???
Qualifications should include a positive attitude to learn and participate in novel applications of various sciences we are touching on. Some tasks are straight forward, others can be quite complex, all are of equal importance in the big picture as all are building blocks for the other things. Each member of the team will work on projects of interest and aptitude.
If interested, apply with a resume and a cover letter to Our website is

Postdoctoral Fellow in Plant Population Genomics
University of Wisconsin, Madison
A postdoctoral position is available in the laboratory of Shelby Ellison, in the Department of Horticulture at the University of Wisconsin, Madison. The Ellison lab conducts research on plant domestication, genetics, and breeding on alternative crops. Major areas of research include determining the genetic basis of traits selected for during domestication and improvement, characterizing germplasm to maximize in situ and ex situ preservation efforts, investigating the role of genotypic diversity and phenotypic plasticity in plant adaptation, and developing novel phenotyping methodologies for emerging crops.

This opening is for a highly motivated postdoctoral fellow with expertise in one or more of the following areas: genetics, genomics, population genetics, molecular biology, quantitative genetics, plant research, field research. All candidates must have received a Ph.D. in a relevant field. The postdoc will be based at the University of Wisconsin, Madison campus and have access to all the resources available through the Horticulture Department. The position is available for 1 year with the possibility of renewal for a second and third year and will include a competitive salary and full benefits.

Interested candidates please contact Shelby Ellison at Applications should include: a cover letter, a brief description of past research accomplishments and future research goals, CV, and contact information for three references. To learn more about the Ellison Lab, visit our webpage at For more about the University of Wisconsin, Madison, please see here:

Basic Qualifications:
Ph.D. in plant breeding, bioinformatics, quantitative/statistical/population genetics, or a related discipline
Experience with project management and experimental design
Demonstrated expertise analyzing highly-dimensional genomic and phenomic datasets
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