Postdoctoral Associate

Position Function:

The selected candidate will join a large, multi-disciplinary project that seeks to develop engineered technology and computer algorithms that increase sustainability in row crop production systems. Our team includes faculty and researchers drawn from the Geosystems Research Institute at Mississippi State University (MSU), the MSU Department of Computer Science and Engineering, and the United States Department of Agriculture – Agricultural Research Service (USDA – ARS). Specific to the role of the postdoctoral associate will be supporting continued development of a robotic system for monitoring soil carbon and greenhouse gas (GHG) emissions in agricultural landscapes. The associate will be expected to work with both uncrewed ground vehicles (UGV) and uncrewed aerial vehicles (UAV) in a collaborative robotics system. Collected data will then be used in conjunction with machine learning (ML) models to perform field-scale soil carbon sensing and monitoring.
This position is located at the main campus of Mississippi State University near Starkville, Mississippi, USA. Remote candidates and telework candidates will not be considered. This research is funded by the USDA-ARS; the position is contingent upon continued funding.
• Interdisciplinary and collaborative research environment
• Applied engineering in a real world setting
• Opportunity to contribute to vital research to quantify and mitigate climate change
• Opportunity to work with cutting-edge technologies in machine learning and robotics

Salary Grade:  UC

For salary grade UC, these positions are "Unclassified" and salary ranges are determined by the hiring department.

Department Profile:

A land grant university, Mississippi State University (MSU), is among the < 3% of US universities that hold the Carnegie Foundation R1-Very High Research Activity designation. The National Science Foundation additionally ranks MSU among the nation’s Top 100 research institutions for total research and development spending. In 2022, MSU received its third prestigious Higher Education Excellence in Diversity Award for institutional efforts to promote a culture of inclusion.

GRI is an interdisciplinary collaborative of academic scientists, engineers, and government and industry stakeholders active in conducting research to advance knowledge and practice on earth and its systems to improve policy and public awareness. All personnel at GRI have a core focus on geospatial technologies and their application to natural resource management. GRI is located within the Portera High Performance Computing Collaboratory within the Cochran Research, Technology, and Economic Development Park on the north side of the MSU main campus.

Area of Specialization:

Climate Smart Agriculture

Anticipated Appointment Date:

July 1, 2024

Essential Duties and Responsibilities:

• Complete development of a prototype collaborative UAV-UGV system for monitoring soil carbon and greenhouse gas (GHG) emissions in agricultural landscapes following specifications provided
• Operate collaborative UAV-UGV system as requested to support data collection from field experiments and lead associated data analysis
• Develop an ML model(s) to describe GHG fluxes based on experimental data
• Publish experimental results in appropriate scientific journals, and present the research work at national and regional conferences.

Minimum Qualifications:

• PhD in Computer Science, Robotics, Mechanical Engineering, Electrical Engineering, Precision Agriculture, Agricultural Engineering, Plant and Soil Sciences, or other closely related field.

ABDs or degree pending considered (all but DISS):

No

Preferred Qualifications:

• Prior experience with field and/or agricultural robotics, including operation and development, and specifically with UGV and UAV
• Prior experience working with chemical sensors (e.g., non-dispersive infrared sensors, visible and near-infrared (Vis-NIR) spectroscopy sensors)
• Prior experience conducting in situ samping in agricultural field environments
• Prior experience working in a lab setting, specifically related to processing samples for soil carbon
• Proficiency with classical statistical techniques (e.g., regression) in addition to machine learning methods (e.g., physics-informed deep learning and PDE networks)
• Proficiency in a programming language such as Matlab, Python, R, C++, and/or ROS
• Excellent written and oral communication skills
• Track record of quality research publications in relevant discip