| Program start date | Application deadline |
| 2024-02-26 | - |
| 2024-07-15 | - |
| 2025-03-03 | - |
| 2025-07-21 | - |
Program Overview
The Master of Mathematical Modelling (MMathModel) at the University of Auckland is a postgraduate program that equips students with advanced mathematical and computational techniques for real-world problem-solving. This cross-faculty collaboration between the Department of Mathematics and the Department of Engineering Science emphasizes a multidisciplinary approach, preparing graduates for careers in data analysis, research, and other quantitative fields. The program offers both 120- and 180-point pathways, providing flexibility for students with varying backgrounds and career goals.
Program Outline
Master of Mathematical Modelling (MMathModel) - The University of Auckland
Degree Overview:
Overview:
The Master of Mathematical Modelling (MMathModel) is a postgraduate program designed to equip students with advanced mathematical and computational techniques for real-world problem-solving. This program focuses on real-world applications such as modelling transport networks, epidemics, and climate change.
Objectives:
- Equip graduates with advanced mathematical and computational techniques for real-world problem-solving.
- Build expertise in advanced computational tools, fundamental theoretical principles, and underlying assumptions.
- Prepare students for careers in data analysis, research, and other quantitative fields.
Program Description:
The MMathModel is a cross-faculty collaboration between the Department of Mathematics and the Department of Engineering Science. It is offered with both 120- and 180-point pathways, providing flexibility for students with varying backgrounds and career goals. The program emphasizes a multidisciplinary approach, equipping graduates with a strong foundation in both mathematics and real-world applications.
Outline:
Core Courses:
The program comprises four core courses that lay the foundation for mathematical modelling principles and applications:
- MATHS 765: Mathematical Modelling
- MATHS 787: Special Topic: Inverse Problems and Stochastic Differential Equations
- ENGSCI 711: Advanced Mathematical Modelling
Elective Courses:
Students can choose from a broad range of elective courses focusing on specific areas of interest, including:
- Engineering Science: Computational Algorithms for Signal Processing, Advanced Mechanics in Research and Technology, Advanced Modelling and Simulation in Computational Mechanics, Decision Making in Engineering, Algorithms for Optimisation, Integer and Multi-objective Optimisation, Advanced Simulation and Stochastic Optimisation, Advanced Optimisation in Operations Research, Advanced Operations Research and Analytics.
- Mathematics: Dynamical Systems, Nonlinear Partial Differential Equations, Advanced Partial Differential Equations, Inverse Problems, Stochastic Differential and Difference Equations, Advanced Numerical Analysis.
- Physics: Advanced Statistical Mechanics and Condensed Matter, Waves and Potentials, Photonics, The Dynamic Universe, Quantum Optics and Quantum Information, Advanced Imaging Technologies.
- Other: Musculoskeletal and Orthopaedic Biomechanics, Econometrics 1, Econometrics 2, Research Methods – Modelling.
Research Project:
All students undertake a 45-point research project, allowing them to apply theoretical knowledge to real-world problems and develop independent research skills.
Assessment:
The program utilizes diverse assessment methods to evaluate student learning outcomes, including:
- Assignments: Problem sets, essays, and case studies to assess comprehension and application of theoretical concepts.
- Examinations: Written exams to assess understanding of core knowledge and problem-solving skills.
- Presentations: Oral presentations to develop communication and critical thinking skills.
- Research Project: A comprehensive project, assessed through written reports and presentations, to demonstrate independent research capabilities.
Teaching:
The MMathModel program adopts a multifaceted approach to teaching, incorporating lectures, tutorials, workshops, and project supervision. This ensures a strong foundation in theoretical concepts, practical application, and hands-on experience.
Faculty:
The program draws on expertise from leading researchers and academic staff in mathematics, engineering science, and related fields. Their combined knowledge and experience contribute to a high-quality and insightful learning experience.
Teaching Methods:
The program employs interactive teaching methods, including:
- Problem-based learning: Students actively engage in solving real-world problems using mathematical and computational tools.
- Collaborative learning: Group projects and discussions encourage team-based learning and peer-to-peer interactions.
- Case studies: Real-world examples illustrate theoretical concepts and their practical applications.
Careers:
Career Opportunities:
The MMathModel equips graduates with a wide range of transferable skills applicable to diverse career paths. They are well-prepared for roles in data analysis, research, modelling, consulting, and other quantitative fields.
Potential Employers:
- Government agencies: Ministry of Health, Ministry for the Environment, Statistics New Zealand
- Research institutions: The Alan Turing Institute, SINTEF, University of Oxford
- Private companies: Google, Amazon, McKinsey & Company
Specific Career Examples:
- Data Scientist
- Statistician
- Operations Research Analyst
- Mathematical Modeller
- Research Scientist
- Consultant
- Quantitative Analyst
Other:
Program Duration:
The program can be completed in 1 year (for 120-point pathway) or 1.5 years (for 180-point pathway).
Locations:
City campus
Entry Requirements:
- Bachelor's degree in relevant field (analytics, applied mathematics, artificial intelligence, computer science, data science, engineering, operations research, physics, software engineering, structural engineering, electrical engineering, statistics, technology) with a GPA of 4.0 or higher.
- Relevant coursework in mathematics, computer programming, and partial differential equations.
Language Requirements:
With a focus on real-world applications and advanced tools, this program equips graduates for impactful careers in data-driven fields.
