Applied Mathematics - Grangegorman

What is... Applied Mathematics?

The MSc in Applied Mathematics is a taught postgraduate course delivered by the School of Mathematics & Statistics. It offers a broad range of topics in applied mathematics and applied statistics and is suitable for applicants from a wide variety of backgrounds. It is designed to cater for those who wish to develop an advanced level of mathematical and statistical knowledge and practical skills relevant to problem solving in diverse, real-world contexts, and modelling is at the heart of the programme.



The course is undertaken over 3 semesters (approximately 18 months), starting in September. The programme is structured to suit those that are combining study with a career or family life and is a blend of evening online (synchronous and asynchronous) delivery and evening in-person on campus block delivery. The course encourages students to engage in autonomous, self-directed learning whilst providing a supportive environment where lecturers and year tutors facilitate and mentor learning.



The course will emphasise the use of mathematics and statistics to solve problems and will build students' knowledge and their ability to analyse problems and apply advanced mathematical and statistical techniques in a rigorous manner. Successful applicants will require a primary degree at second-class or higher classification and will be drawn from computing, engineering, mathematics, physics, statistics and other scientific and numerate backgrounds. Graduates of MSc Applied Mathematics will be flexible, highly qualified, technical professionals with advanced analytical and problem-solving skills suitable to enter, and apply their knowledge in, a wide range of sectors and professions.



Course Content

The programme MSc Applied Mathematics comprises nine taught modules followed by a dissertation which is undertaken in Semester 3. The course comprises a student workload of 90 ECTS credits and the project contributes 25 ECTS credits. Students also undertake a Global Citizenship in the Workplace module which develops the knowledge and skills required to work and lead in diverse workplaces and systematically approach global challenges. Learning is supported through software, group learning, supported practical sessions, seminars and the student library and study facilities.



An exit award of Postgraduate Diploma (60 ECTS credits) is available.



The following topics are covered in the taught modules:

- Numerical Methods and Machine Learning for Differential Equations

- Biomathematics

- Modern Regression Modelling

- Computational Statistics

- Mathematical and Statistical Modelling with Case Studies (Semester 1 & 2)

- Global Citizenship in the Workplace

- Software Laboratory

- Research Skills



The project is a substantial piece of academic written work and will normally be based upon a topic closely related to modules on the programme and may include an extensive literature review. The Research Skills module provides an excellent preparation for the project module and students are supported in this module by an assigned academic supervisor and their peers.



The taught modules are assessed either by a combination of written examinations and continuous assessment (e.g., MCQs, problem-based assignments, case study reports) or solely based on continuous assessment. Some modules may also involve practical tasks, e.g., coding exercises. The assessment of the project module is based upon the written work, feedback and participation during the module and a viva voce examination.



Continuous assessment is undertaken during the semester and written examinations for Semester 1 modules take place in January and for Semester 2 modules take place in May. Reassessment takes place in August.



Note: Students are required to attend the University in-person to sit the written examinations.

Module listing:

Year One

Semester 1

Biomathematics [Mandatory]

Introduction to Mathematical & Statistical Case Studies [Mandatory]

Numerical Methods and Machine Learning for Differential Equations [Mandatory]

Project [Mandatory]

Research Skills [Mandatory]

Software Laboratory [Mandatory]



Semester 2

Global Citizenship in the Workplace [Mandatory]

Mathematical & Statistical Modelling with Case Studies [Mandatory]

Algorithms & Approximation Theory [Elective]

Classical Mechanics and Fluid Mechanics [Elective]

Computational Statistics [Elective]

Methods for Applied Mathematics [Elective]

Modern Regression Modelling [Elective]

Special Relativity & Tensor Calculus [Elective]

Minimum Entry Requirements?

Students wishing to enrol should normally possess a minimum of the equivalent of an honours degree (level 8 on the NFQ) at Grade 2.2 or above in a highly quantitative discipline such as computing, engineering, mathematics, physics, statistics and other scientific and numerate backgrounds.



The relevance and mathematical content of an applicant’s primary degree and other experience will be assessed by the admissions team for the programme whose decision is final. Attainment of the minimum entry requirements does not guarantee entry to the course and all candidates will be assessed against the entry criteria, in the context of the available places on the course, for their prior learning and on their ability to succeed.



If English is not your first language you will need to provide evidence of your English language proficiency as detailed on our website. Applicants for this programme should have a minimum IELTS (Academic Version) English Proficiency of 6 overall (or equivalent) with not less than 5.5 in writing.

How to Apply

Applications for this course are now open.



Apply Online - FULL-TIME EU

Apply Online - FULL-TIME NON-EU



For further information on the application process, please visit How To Apply (see "Application Weblink" above).

1 - 1.5 years full-time, blended delivery.



Schedule

Each module is delivered over one semester and online synchronous (live) and asynchronous (pre-recorded) lectures and tutorials are held two evenings per week (from approximately 6.30pm). The Mathematical and Statistical Case Studies; Software Laboratory and Research Skills modules are delivered in-person in the evenings, in blocks four times a semester and are also supported through online learning.



Semester 1

Monday (Synchronous/Asynchronous Online)

18:30 - 22:00



Tuesday (Synchronous/Asynchronous Online)

18:30 - 22:00



Wednesday (On-campus. 4 times a semester)

18:30 - 22:00

Semester 2



Tuesday (Synchronous/Asynchronous Online)

18:30 - 22:00



Wednesday (On-campus. 4 times a semester)

18:30 - 22:00



Thursday (Synchronous/Asynchronous Online)

18:30 - 22:00

Commencement Date: September 2025.

What are my career opportunities?

Graduates of the course are extremely flexible and able to apply advanced mathematical and statistical techniques and problem-solving approaches to a wide variety of problems arising in careers in a diverse range of employment sectors.



In particular, graduates of this course have enhanced technical and scientific capabilities, analytical and problem-solving skills, and are well equipped for high-level careers in industry, commerce, the professions and the public sector.



Applied mathematics and statistical research is a strength of the School of Mathematics & Statistics and there are also opportunities for graduates to undertake further research in the TU Dublin or elsewhere.