Position: Postdoctoral (Research) Scholar.

Project: Development of point-measurement pasture canopy-height sensor.

Required Minimum Qualification: Doctoral degree.

Preferred Qualifications:

• Doctoral degree in Agronomy, Engineering, Statistics, Computer Science or related field.
• Experience with data collection, wrangling, analysis and reporting.
• Experience that demonstrates independent coordination of multiple activities.
• Experience with a statistical programming language (preferably R or Python).
• Experience with fast prototyping (microcontroller and microcomputers, 3D printing, CAD, electronics,
• and version control).
• Experience with robotics, using IMUs, GPS, sensor integration, data fusion techniques (e.g. Kalman
• Filters) and controls (e.g. PID).
• Experience with SQL databases (e.g. MySQL, PostGres and PostGIS) or other database servers.

The successful candidate will:

• Collaborate with the University of Missouri Forage-Livestock Group to expand and enhance the current
• PaddockTrac and GrazingWedge projects.
• Lead the development of a time of flight (e.g. point-measurement sonar or lidar) sensor to estimate
• forage canopy height (PaddockTrac).
• Assist on the dissemination of this sensor across end-users (i.e. farmers). Collaborate with the successful
• data stream from proximal sensor to the University of Missouri databases (Grazing Wedge).
• Collaborate towards the development of a front-end platform (GEE app) to display geospatial within
• paddock forage biomass.
• Write research grants and disseminate findings through conferences or scientific journal publications.
• Supervise undergraduate and postgraduate students.

Project:

Context: PaddockTrac is a mobile application which allows users to connect to a Bluetooth sensor to

collect data for use in the calculation of dry matter in a paddock for use in the Grazing Wedge website.

The Grazing Wedge is a tool for managing forage in grazing systems. It visually represents the quantity of

forage dry matter available per acre or hectare at a single point in time. The grazing wedge enables beef

and dairy producers to make forage management decisions that align to their production goals.

These two projects have been developed for the past ten-years and have an extensive dataset from

which canopy-height vs. biomass can be modelled using the appropriate machine learning techniques.

The data stream from PaddockTrac (i.e. proximal sensor) and GrazingWedge can and should be

employed to the development of remote sensing models to enable satellite-based forage biomass

estimation.

Expected starting date: June or July 2022.

Appointment: one-year with possible extension to a period of three-years.

The successful candidate will be under the leadership of Dr. Rob Kallenbach (Associate Dean of the

College of Agriculture, Food and Natural Resources). Review of the applications will begin immediately

and will continue until position is filled. For more information, please contact Dr. Kallenbach (Rob’s

email)

Location: The University of Missouri is located in Columbia (MO - USA)

Remuneration: This role offers remuneration as per the candidate’s experience while also offering full

fringe benefits. An ideal candidate will be offered a annual salary of $60,000.

To Apply:

Please forward:

 cover letter describing your relevant experience, research interests, and why you are interested

in this position,

 your CV, and

 list of two references.

Please address your application to:

Rob Kallenbach: KallenbachR@missouri.edu

Ryan Lock: LockT@missouri.edu