Opportunities

Background: 

Radiata pine breeding company (RPBC) plays a central role in breeding elite genetic material to forest owners in Australasia, with emphasis on production traits. However, with changing climatic conditions a new set of challenges arise, for instance, increased drought and disease susceptibility. Among diseases, dothistroma needle blight (DNB) and red needle cast (RNC) are significant as they negatively affect productivity. High-throughput phenotyping for drought and disease resistance is of key interest to RPBC for breeding next generation of trees that are resilient to biotic and abiotic stresses.

The project: 

• The University of Canterbury (UC) is working alongside RPBC to develop remote sensing methods for high-throughput phenotyping, reducing the costs of field-based assessments to accurately monitor and quantify these drought and disease resistance traits. The methods developed will ultimately result in developing tolerant genotypes through selective breeding.

• In this project, RPBC and UC want to explore the feasibility of using multispectral and thermal imaging technologies for high-throughput phenotyping of drought and disease progression in radiata pine. A combination of controlled experiments and field-based assessments will be carried out, coupled with advanced quantitative genetic analysis to quantify the learnings from both systems. These findings will help to develop a robust pipeline for phenotyping adaptive traits in the RPBC breeding programme.

Who we’re looking for: 

The University of Canterbury wishes to hire a postdoctoral researcher (3-year contract, 1.0 FTE at between $61,007 – 91,511 per annum, depending on experience) who has demonstrated research capabilities in remote sensing image acquisition, processing, and analysis. Experience with deep learning and time-series/phenological datasets is desirable. An understanding of vegetation biology and function is preferred, but not necessary. 

The successful candidate should be willing and able to begin work prior to 1 June 2024. The role requires the candidate to be based in Christchurch, New Zealand.

Who we are: 

You will work alongside research and technical staff at the Radiata Pine Breeding Company (rpbc.co.nz) and researchers and graduate students at the University of Canterbury’s Remote Sensing and Geospatial Analysis research group (rsga.co.nz). Questions and expressions of interest should be sent to Assoc. Prof. Justin Morgenroth (justin.morgenroth@canterbury.ac.nz).

We are excited to offer a PhD position in the field of remote sensing at the New Zealand School of Forestry, University of Canterbury. This research project aims to advance our understanding of minor forest species in New Zealand by using image analysis and machine learning/deep learning. We are seeking a motivated and enthusiastic PhD candidate to join our team and contribute to cutting-edge research in forestry.

Description: 

New Zealand’s plantation forests are predominantly composed of a single species - Pinus radiata. However, concerns have arisen about the risks associated with relying heavily on a single species, such as market demand fluctuations and the threat of devastating pest or disease outbreaks. Hence, there is growing interest in diversifying forest resources across the country. To effectively model the potential sustainable log supply from these minor species, it becomes crucial to accurately assess the extent and distribution of the existing resource. However, such information is currently lacking, as most of these forests are privately owned, small and fragmented.  Therefore, this research will focus on developing innovative remote sensing techniques to identify, classify, and monitor these minor tree species throughout the country. By combining advanced image analysis and machine learning/deep learning methods, we aim to provide accurate and efficient tools to monitor and manage these valuable resources.

Requirements:

We are looking for a highly motivated individual with a strong background in remote sensing, forestry, or a related field. The ideal candidate should have the following qualifications:

• A Master's degree (or equivalent) in forestry, environmental science, geospatial science, remote sensing or a related discipline.

• Meet the English language requirements for admission for international students (UC requirement). 

• Proficiency in remote sensing techniques, including image processing and analysis, machine learning and deep learning. 

• Experience with programming languages such as R and Python.

• Strong analytical and statistical skills.

• Excellent communication and writing abilities.

Funding and support: 

The successful candidate will have the tuition fee waived and will be supported by a competitive stipend (NZD$28,000) for the duration of the PhD program.

How to apply: 

• Please submit the following documents when applying: 

• A CV including your research experience 

• Academic transcripts

• A cover letter outlining your research interests and motivation to join the project

• Contact information for two academic referees

• Submit all required documents to Dr Vega Xu (cong.xu@canterbury.ac.nz)

Application Deadline:

The application deadline is 30th Sep 2023.

Shortlisted candidates will be contacted for interviews within one month after the application deadline.

For further inquiries about the project or application process, please contact Dr Vega Xu (cong.xu@canterbury.ac.nz).

We look forward to welcoming an enthusiastic and dedicated PhD student to join our team in Christchurch, New Zealand.