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 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).
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 required
Qualifications: 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. Muthu Bagavathiannan, Texas A&M University, College Station, TX (firstname.lastname@example.org)
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