The Massey University is pleased to offer the PhD Position: Characterizing Honey Composition. The program is open to students who have a master’s degree (or equivalent) in food or process engineering, computational science or similar discipline with a focus on applied mathematics and computing.
The eligible candidates will work in a research team drawn from the School of Food and Advanced Technology, the Massey AgriTech Partnership, AgResearch, and the project’s industry partners. They will work mainly on the research-intensive and beautiful Palmerston North campus, amongst a vital team of professors, young researchers and PhD students.
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- Applications Deadline: Closed
- Course Level: PhD
- Study Subject: Food Science, Spectroscopy, Machine Learning & Artificial Intelligence
- Scholarship Award: The successful candidate will receive a FIET scholarship covering fees and stipend for three years.
- Nationality: Internationally
- Number of Scholarships: Not given
- Scholarship can be taken in New Zealand, Palmerston North
Eligibility for the Scholarship:
To be eligible, the applicants must be following all the given criteria:
- Eligible Countries: Specifically not given, contact employer
- Entrance Requirements:
- Master’s degree (or equivalent) in food or process engineering, computational science or similar discipline with a focus on applied mathematics and computing.
- Familiarity with at least one programming language (e.g., R, Matlab Python).
- Experience in data and image analysis, ideally machine vision techniques.
- Required personal skills
- The ability for independent work displaying initiative and careful thought
- The analytical and academic approach to research questions
- Good collaborative/social skills
- Proficiency in English, both written and spoken
To apply, the applicants must send your CV and cover letter in a single PDF to Dr Reddy Pullanagari,
[email protected] with the subject “Characterising honey composition by hyperspectral analysis PhD position”.
- Predictions using known honey samples under laboratory conditions. Several hundred known honey samples are available for inspection using several hyperspectral cameras to develop efficient predictions based on a few spectral lines. Different data mining techniques may be explored to develop robust prediction models.
- Application of predictions to whole frames in the laboratory setting: Image processing techniques will be needed to isolate pixels for an individual cell, a task supported by a post-doctoral researcher also working in the project. The PhD will determine how best to use data from pixels allocated to a cell.
- Development of a prototype for use in the industrial setting: The PhD researcher will assist the post-doctoral fellow to develop an industrial unit by applying the predictive tools developed. The PhD would address more fundamental issues and the post-doc address more applied ones.
- Fluorescence: The phase addresses how best to use the hyperspectral camera to capture the fluorescence signature cell by cell. This will require special illumination and may need a sequence of illuminations and image captures, thus introducing a temporal element to the technique.