A research position (at post-doctoral level) is available at the Weed Science Research Laboratory, Texas A&M University, College Station, Texas, USA to develop machine learning/artificial intelligence solutions for weed detection, classification and mapping to facilitate precision weed management using field robotics/drones in various agricultural systems. The successful applicant will join an interdisciplinary team of researchers investigating Integrated Weed Management, Remote Sensing, Precision Agriculture, Computer Vision, Deep Learning and Field Robotics. The incumbent will work collaboratively with a national team of scientists as part of GROW (Getting Rid Of Weeds; https://growiwm.org/) and PSA (Precision Sustainable Agriculture; http://precisionsustainableag.org/) networks. Key partners include Dr. Steven Mirsky (USDA-ARS, Beltsville) and Dr. Chris Reberg-Horton (North Carolina State University).
Key responsibilities:• Develop methodological framework for field experiments• Manage the acquisition, handing and processing of huge database of images• Analyze images using state-of-the-art computer vision techniques and machine learning algorithms• Manuscript preparation and publication in peer-reviewed scientific journals• Mentor graduate students and provide technical support to relevant projects• Assist with securing extramural funding to the program
Skills and Expertise:• Data mining, quantitative analysis, visualization tools• Machine learning algorithms (including current deep learning techniques)• 2D and/or 3D computer vision techniques• Predictive modeling and decision analytics• Basic remote sensing techniques• Knowledge of the application of field robotics and/or unmanned aerial systems in agricultural or natural systems are preferred
Softwares and programming languages:• C++ or Python• PyTorch, TensorFlow or similar deep learning libraries• Image processing libraries such as OpenCV• Big data handling using Matlab, Python, R or other analytical platforms• ArcGIS or other geospatial data handling software• ERDAS Imagine, eCognition Developer, ENVI, or other image processing software
Broad knowledge of the above tools is an asset, but consideration will also be given to candidates who have a good background and demonstrate the ability to quickly learn specific tools as requiredQualifications: A relevant training at PhD level (MS degree holders with years of relevant experience may also be considered). Demonstrated written and oral communication skills are essential and previous experience working in agricultural or natural systems are preferred.
Start Date: As soon as a suitable candidate is identified.
Application: Interested applicants should email a detailed CV describing relevant knowledge and experience, list of publications, pertinent accomplishments and contact details of three referees to Dr. Muthukumar Bagavathiannan, Texas A&M University, College Station, TX (email@example.com)