Overview: In the Diaz Laboratory, we strive to give the best career-building opportunities for our team members in a diverse and inclusive environment. This comprises exposure to different projects on invasive species and biological control, training on field and laboratory techniques, exposure to international cooperation of research projects, mentoring visiting scholars and undergraduate students, establishment multidisciplinary projects, hands-on preparation of technical and non-technical publications, among others.

We are seeking a graduate student to join our team and contribute to a cutting-edge project on improving the biological control of giant salvinia (Salvinia molesta). Early detection of salvinia infestations using remote sensing could help target releases of salvinia weevils (Cyrtobagous salviniae), and consequently impact the growth of salvinia.

The successful candidate will conduct research at the intersection of artificial intelligence (AI), remote sensing, and entomology to optimize the effectiveness of salvinia control. The availability of satellite data could allow the development of near-real time monitoring of invasive species in aquatic habitats.

This position offers an exciting opportunity to advance applied knowledge, make a tangible impact on the management of invasive species, and for career building. During this project, the student will collaborate directly with scientists and managers from Barataria-Terrebonne National Estuary Program, The Coastal Wetlands Planning, Protection and Restoration Act, and Center for Biological Control at Rhodes University, South Africa.


  • Conduct a literature review to understand the background and existing methodologies for remote sensing of wetlands, management of aquatic weeds, and plant damage by insect herbivores.
  • Design and implement experiments to measure spectral reflectance of salvinia at different damage levels.
  • Develop machine learning algorithms and models to analyze remote sensing data, such as satellite imagery and unmanned aerial vehicle (UAV) data, for salvinia detection, mapping, and biomass estimation.
  • Collect and analyze field data to validate and refine the developed models and algorithms.
  • Implement a web-based interface, with software such as Google Earth Engine, showing near-real time monitoring data on salvinia coverage, and damage levels (NDVI) for several waterbodies in Louisiana.
  • Collaborate with multidisciplinary teams, including ecologists, entomologists, and remote sensing experts, to integrate AI and remote sensing techniques into existing salvinia control strategies.
  • Communicate research findings through technical reports, scientific publications, and presentations at conferences and meetings.


  • Bachelor’s degree in a relevant field such as entomology, environmental science, ecology, earth sciences, computer science, or a related discipline.
  • Strong interest in GIS, remote sensing, machine learning, and ecological systems.
  • Experience in or willingness to learn Google Earth Engine, as well as other GIS software (QGIS, ArcGIS, etc.).
  • Experience in or willingness to learn machine learning techniques and algorithms, such as image segmentation and object detection.
  • Experience in or willingness to learn programming languages commonly used in AI and remote sensing, such as Python, R, and JavaScript. This includes the use of large language models (such as ChatGPT) to improve writing of code.
  • Excellent problem-solving skills and ability to work independently.
  • Strong written and verbal communication skills, with the ability to present complex ideas in a clear and concise manner.
  • Prior experience in conducting ecological fieldwork and data collection is desirable.

Application Process: Please submit the following documents to Dr. Rodrigo Diaz at rdiaz@agcenter.lsu.edu:

  • A cover letter outlining your research interests, relevant experience, and why you are interested in this position.
  • A detailed curriculum vitae (CV) showing your experience.
  • Contact information for three references.

Please email these materials as a single PDF file to rdiaz@agcenter.lsu.edu with the subject line “Remote Sensing – Salvinia Biological Control” by August 30, 2023. Applications will be reviewed until the position is filled.

For more information about biological control of giant salvinia, and the Diaz Laboratory please visit: www.lsuagcenter.com/giantsalvinia Equal Opportunity Employer

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