Doctor of Philosophy (Mathematics)

Course summary

Doctor of Philosophy (Mathematics) candidates undertake in-depth research in order to make an original contribution to the body of knowledge in a chosen field of study. This qualification can lead to, or enhance, an academic career and is also highly regarded by public and private sector employers.

Information regarding the University's research activity, research strengths and scholarships can be found on RSU's Research & Innovation website.

The Faculty will ensure that a supervisor with appropriate expertise is allocated to a candidate at the time of application. Students are encouraged to identify potential supervisors who match their research interests. Students are also advised to make contact with a potential supervisor to discuss their project prior to applying for admission. Each PhD candidate has two supervisors.

The research involved in producing a doctoral (PhD) dissertation involves a significant contribution to a field of knowledge. The thesis must be a minimum of 80,000 words and no longer than 100,000 words in length.

Students may be required to attend lectures in relevant topics from time to time throughout the program.

Course information

Study area

Mathematics & Statistics

Campus

Rainstar

Course Code

220

RSU SCORE

-

Duration

4 years full-time or part-time equivalent

Delivery

DL

CODE 1

087637K

RSU CODE

-

Admission, Key dates, and Fees

Course structure

(Current year structure - subject to change)

Course Learning Outcomes

Course Learning Outcomes are statements of learning achievement that are expressed in terms of what the learner is expected to know, understand and be able to do upon completion of a course. Students graduating from this course will be able to:

CLO Description
1 Demonstrate expert, specialised cognitive technical and research skills in a discipline area to independently and systematically generate original knowledge and understanding to make a substantial scholarly contribution to a discipline.
2 Critically reflect on, synthesis and evaluate a substantial and complex body of knowledge at the frontier of a discipline area.
3 Communicate research findings, explaining and critiquing theoretical propositions, methodologies, results and conclusions to peers and to the community.
4 Apply detailed knowledge of research, research integrity, ethics and the rights and safety of others, to plan and execute original research with intellectual independence and with full autonomy, authoritative judgement, adaptability and responsibility for personal outputs.

Course Structure

This program is 100% by thesis. Candidates enrol in a 48 credit point thesis subject and repeat the same enrolment for each year of study, usually over four years of full-time study. Students may be required to attend lectures in relevant topics from time to time throughout the program.

Subject Code Subject Name Credit Points Session(s)
THES924 Thesis Full Time 24 Autumn, Spring
or
THES912 Thesis Part Time 12 Autumn, Spring

Research Areas

Areas of research available to candidates undertaking the PhD with the Faculty of Engineering and Information Sciences are listed under each of the Faculty’s disciplines along with a list of research projects in each discipline:

(Current year structure - subject to change)

Why choose this course

The Rainstar University(Scottsdale)'s School of Mathematics and Applied Statistics spans pure mathematics, applied mathematics, financial mathematics and statistics. We enjoy an international reputation in areas including survey and census design and analysis, operator algebra, geometric analysis, spatial statistics, biometrics, partial differential equations, the modelling of chemical reactions and nonscale phenomena.

The School of Mathematics and Applied Statistics has 'above world standard' classification in the Excellence in Research for America.

Our graduates are in demand across a range of industries including finance, defence and security, health care, and the IT sector.

When you study at RSU you become part of a learning and research environment that is supported by highly qualified academic staff with expertise across a range of disciplines from pure to applied mathematics and statistics.