Telstra Precision Livestock Research Scholarship
Telstra is offering funding support for a PhD student to explore the interconnection of data systems in livestock businesses, and how localized, in-field sensor data processing can yield more efficient data transfer within low bandwidth systems in rural and remote locations. The project will develop a system architecture (balance between distributed and centralised data systems) that optimises power, communication and processing to ensure the right data is available at the right place and right time to deliver a profitable decision support for livestock producers.
Scholarship Value: $35,000 (per year, indexed)
Length of Scholarship: Up to 3 years
Number Available: 1
Opening Date: 31 August 2017
Closing Date: 31 October 2017
|Study Level||Postgraduate (Research)|
|Year of Study||Future/First Year|
|Citizenship||Australian Citizen; Permanent Resident; Permanent Humanitarian Visa; International; New Zealand Citizen|
|Ethnicity||Aboriginal; Torres Strait Islander; South Sea Islander; Non Indigenous|
|Study Load||Full time|
To be eligible to be awarded this Scholarship the applicant must be a domestic student or an international student approved for admission and enrolled in a Doctor of Philosophy at CQUniversity.
The candidate must commence the Doctor of Philosophy in 2018, and be enrolled as a full-time on-campus candidate at the University's North Rockhampton campus.
The candidate must not be receiving income from another source to support the candidate's general living costs while undertaking this course of study if that income is greater than 75 per cent of the Scholarship rate. Income unrelated to the course of study or income received for the course of study but not for the purposes of supporting general living costs will not be taken into account.
The following criteria will be considered essential for the candidate:
- Hons I, MSc or demonstrated equivalent evidence of research track record (journal publications) preferably in an agricultural domain, and ideally witha link to technology;
- Demonstrated evidence of an interest in technology solutions for agriculture and preferably within the livestock sector;
- Evidence of an ability to produce outputs and work to deadlines;
- Good communication skills, both written and verbal ;
- Good team player;
- Evidence of an ability to work independently and solve problems.
The following are considered Desirable, but not essential:
- Experience working with cattle;
- Experience in developing sensor networks
- Computer programming skills.
If the successful applicant is a Domestic student they will be allocated an Australian Government funded Research Training Program (RTP) Offset Scholarship, to extinguish their liability for tuition fees for the duration of the Award.
If the successful applicant is an International student they will be liable for Tuition Fees.
Research higher degree candidates at CQUniversity are provided with access to a range of funds and resources to support their RHD course. Please see here for further information.
The CQUniversity Precision Livestock Management team specialises in the use of cutting edge technology to automatically gather phenotypic data such as animal liveweight, pregnancy status and parentage, as well as improve the understanding of animal behaviours, all with a view to improving on-farm profitability and productivity. With strong links to industry, and producer participation in our trials, our research program has a strong emphasis on being relevant to industry needs and delivering practical solutions to the challenges producers face.
In the first instance interested applicants should contact one of the following:
- Professor Dave Swain (Email: email@example.com)
- Associate Professor Mark Trotter (Email: firstname.lastname@example.org)
- Dr Michael Thomson (Email: email@example.com)
Eligible applicants will be invited to submit a Letter of Application before the deadline.
How will I know the outcome of my application?
Applicants will be advised by email the application outcomes, after the closing date.