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Postdoctoral Researcher Position

Applied AI for Forestry Weed Detection and Mapping

College of Science & Engineering
Centre for AI and Data Science Innovation (CADSI)

James Cook University (JCU)

Full-time | One year (with the second year lined up) (Level B - To be confirmed later)

Based in Cairns, Queensland, Australia.

Report to: Dr. Tao (Kevin) Huang, Senior Lecturer & Deputy Director, CADSI

Expected starting time - January 2026

This is a call for EoI before the official advertisement.

For inquiries, please contact Dr. Tao (Kevin) Huang at tao.huang1@jcu.edu.au.

Please put in the email subject: EoI - Postdoctoral Position - Applied AI

Job Description

This industry-sponsored research role offers an exceptional opportunity to work at the frontier of AI, robotics, and environmental sustainability. The successful candidate will help develop intelligent vegetation monitoring systems capable of detecting and identifying weeds in forestry plantations, as well as mapping fire-prone vegetation from aerial imagery.

The project covers the entire innovation journey, from data collection and model development to real-world deployment. A major outcome of this position is the creation of an advanced AI system that will be integrated into an automated robotic platform to guide precision weed removal in forestry environments. This capability will directly support large-scale reforestation and sustainable land management.

Your work will contribute to the development of practical, high-impact technologies that enable more resilient forests, improve seedling survival, and enhance smarter fire-risk mitigation across Northern Australia.

Key Responsibilities

End-to-End Applied AI & System Innovation

·      Build high-quality ground and aerial imagery datasets that capture the complexity of forestry and fire-risk environments.

·      Benchmark and evaluate state-of-the-art deep learning models to uncover opportunities for significant improvements.

·      Develop robust models capable of fine-grained weed identification, seedling–weed differentiation, and fire-risk vegetation classification.

·      Integrate AI models into prototype systems running on automated robotic platforms, enabling the robot to navigate forestry plots and perform precision weed removal.

·      Deploy and evaluate these systems in real field environments, iterating based on performance, environmental complexity, and stakeholder insights.

·      Contribute to the design of vegetation intelligence workflows that directly support operational forestry decisions.

Industry Engagement & Real-World Impact

·      Collaborate closely with the local industry sponsor to shape system requirements and deploy prototypes at real forestry sites.

·      Communicate classification outputs, risk maps, and robotic guidance signals clearly to industry stakeholders.

·      Use industry input to refine AI functionality and robotic interaction strategies.

·      participate in field demonstrations, technical reviews, and collaborative problem-solving sessions.

Research Excellence & Communication

·      Produce high-quality technical and industry reports, dataset documentation, and system design specifications.

·      Publish impactful research in leading venues and present findings at prominent academic and industry events.

·      Support supervision and mentorship of HDR students.

Essential Selection Criteria

·      A PhD (or near completion) in computer science, electrical engineering, AI, machine learning, remote sensing, robotics, or a closely related discipline.

·      Demonstrated expertise in deep learning and computer vision, including experience with semantic segmentation, object detection, classification, or vegetation/weed identification using frameworks such as PyTorch or TensorFlow.

·      Proven ability to work with real-world imagery, including dataset design, annotation workflows, preprocessing, and handling challenging outdoor environmental conditions.

·      Experience developing and evaluating AI models for deployment, including rigorous performance assessment (IoU, F1-score, precision, recall) and translating research models into tools suitable for robotic or field applications.

·      Demonstrated capability to collaborate effectively with industry or external partners, including incorporating stakeholder requirements into system design and communicating technical concepts clearly to non-academic audiences.

·      Evidence of independent research capability, including planning and executing complex tasks, solving technical challenges creatively, and managing project responsibilities with minimal supervision.

·      Strong communication skills, including high-quality technical reporting, academic publishing, and the ability to present research outcomes clearly to both academic and industry audiences.

Desirable

·      Experience with RGB-D or stereo imagery, multimodal fusion, lightweight or real-time inference models, on-robot AI, or related environmental and agricultural computer vision techniques.

·      Experience collecting imagery or environmental data in forestry, agricultural, or outdoor settings, with drone operation capability (RePL/ReOC) considered an advantage.

·      Demonstrated track record of contributing to AI systems used in operational workflows, robotic platforms, or field applications, and experience in industry-funded or industry-collaborative research programs.

·      Experience supervising students or research assistants, along with contributions to grant writing, research proposals, or broader academic project development.

PhD Opportunities in AI for Environmental Monitoring
College of Science & Engineering
Centre for AI and Data Science Innovation (CADSI)

James Cook University (JCU)

We are seeking two candidates for the following two industry-sponsored PhD projects:

·      AI-driven weed detection and precision monitoring for tropical reforestation (Cairns)

·      Machine-learning-based surveillance of harmful cyanobacterial blooms in freshwater reservoirs (Townsville)

Project themes

Both projects focus on developing AI systems that utilize aerial/ground imagery, as well as multisensory environmental data. The work includes model development, field trials, and real-world deployment. Since these projects are supported by industry partners, PhD candidates will engage directly with stakeholders, participate in regular project meetings, and contribute to deliverables that align with industry needs.

What we offer

·      Full-time HDR scholarship: $36,500 per year for 3.5 years (tax-free, indexed)

·      Industry-sponsored projects with strong real-world impact

·      Supervision by experts in AI, computer vision, sensor systems, and environmental monitoring

·      Fieldwork opportunities and close collaboration with government and industry partners

·      A supportive, multidisciplinary research community in a world-class tropical research university

·      Both international and domestic students are welcome to apply

Ideal candidates

We welcome applicants with backgrounds in Computer Science, Electrical/Electronic Engineering, Artificial Intelligence/Machine Learning, Computer Vision, or Remote Sensing. Applicants from related fields (e.g., Mechatronics, Environmental Science/Engineering) may be considered if they can demonstrate strong prior experience in deep learning, computer vision, or sensor data analysis.

Applicants must address the following Essential Selection Criteria (2–3 pages):

1. Academic Qualification: Completion of a First Class Honours degree or a Master by Research (or equivalent). Applicants with a strong Master by Coursework with a research component may also be considered.

2. Deep Learning and Machine Learning Skills: Demonstrated experience with deep learning methods and Python-based ML frameworks such as PyTorch or TensorFlow.

3. Computer Vision or Remote Sensing Experience: Experience working with image or sensor datasets, with foundational knowledge of computer vision or remote-sensing techniques.

4. Research Capability: Evidence of prior research experience (e.g., thesis, project, publication). Applicants with peer-reviewed publications will be considered highly competitive.

5. Communication Skills: Strong written and verbal communication skills, with the ability to clearly explain technical concepts and contribute to collaborative research outputs.

6. Industry Engagement Readiness: Ability and willingness to engage with industry partners, attend regular meetings, and communicate progress professionally. Strong English communication skills are essential, particularly for applicants who are not native English speakers, as the role involves regular interaction with industry stakeholders.

7. Self-Management and Project Skills: Demonstrated ability to work independently, manage milestones, meet deadlines, and maintain consistent research progress.

How to express interest

Please contact:
Dr Tao (Kevin) Huang
Senior Lecturer & Deputy Director, CADSI
Email: tao.huang1@jcu.edu.au
Please put in the email subject: EoI - PhD Position - AI for Environmental Monitoring

Include the following in your application:

·      A focused CV highlighting relevant skills and experience in AI/ML, computer vision, remote sensing, research training, and technical projects

·      Academic transcript

·      A brief statement of your research interests

·      Any publications or thesis work (if available)

·      A short response (2–3 pages) addressing the essential selection criteria

·      Contact details for two referees

·      English test results (if available), or confirmation that you meet JCU’s HDR English proficiency requirements

Please send your EoI by 15 December 2025. Only shortlisted candidates will be contacted and invited for an interview.

PhD/MPhil Candidates

Since you are now reading this section, you must be interested in working with me. This is great! Now you need to see whether you have the following prerequisites:

• Good mathematical background,

• Demonstrated critical thinking ability,

• Demonstrated problem-solving skills, and

• Strong self-motivation.

If you have met all the prerequisites, then your application consists of two phases. If you're unsure how to research a potential supervisor, this link may be helpful.

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Phase 1 - Establish Contact 

In this phase, please send Dr Huang the following documents via email:

• Current curriculum vitae (CV)

• Transcripts and testamurs for all qualifications completed or currently underway

• Self-evaluation against this scholarship scoring procedure (please justify your score)

• Proof of English language proficiency (for international applicants only, if you have one)

• A piece of your most recent writing (if any)

• List of publications (if any)

• Research proposal (if any)

Your submitted files and email will be evaluated carefully. Selected applicants will be organized with a presentation followed by a short interview. Successful candidates will move to the next phase.

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Phase 2 - Submit application 

Please refer to the Graduate Research School website for more information about How to Apply and information about Scholarships. Note that JCU Scholarships for Higher Degrees by Research Candidates are awarded annually on a competitive basis to the top-ranking applicants. Usually, the deadline for the application for a scholarship is the end of August. 

Note that Domestic Applicants means Australian Citizens and Permanent Residents, and New Zealand Citizens. International Applicants are all other applicants. For international applicants, please take a look at the English language requirement and prepare the required documents.

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Students from India: Please send me your GATE score if you have one. The ranking of the universities in India can be found here. 

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Students from China: You may consider applying for the China Scholarship Council (CSC) funding to support your PhD study or visiting study. Don't hesitate to get in touch with Dr. Huang if you intend to apply for the CSC scholarship.