MH6541
Discrete Mathematics, Algorithms and Applications
1.5 AU | This graduate-level course explores discrete mathematics and algorithms with a focus on their applications in various fields. Topics include foundational concepts in logic, set theory, graph theory, combinatorics, and discrete probability. The course covers algorithmic techniques such as sorting, searching, dynamic programming, and approximation algorithms. Applications span combinatorial optimization, network analysis, and computational geometry. Students will gain a comprehensive understanding of theoretical principles and practical methods for solving complex problems. Learning outcomes include mastering core concepts in discrete mathematics and their algorithmic applications, designing and analysing algorithms for solving practical problems, and applying discrete mathematical techniques to real-world challenges in various domains. |
MH6542
Programming Languages and Software Development
1.5 AU | This graduate-level course focuses on equipping students with proficiency in Python and C programming languages essential for tackling coding tasks in their future careers. Students learn key concepts, syntax, and best practices for writing efficient and maintainable code, along with software development practices such as design patterns, version control, and testing. Emphasis is placed on writing robust code and understanding compiler design, runtime systems, and software engineering principles. Through hands-on coding assignments and projects, students will gain practical experience in solving realistic problems using Python and C. |
MH6543
High-Performance Computing and Simulation: MPI, OpenMP and CUDA
3 AU | The course provides an introduction to modern computing platforms, focusing on top supercomputers and accelerators. Students delve into parallel architectures, performance metrics, programming models, and software development challenges. Through case studies of scientific and engineering simulations, students gain insights into real-world applications of parallel computing. Hands-on experience is provided through programming exercises involving multicore processors, graphics processing units (GPUs), and parallel computers. Key topics include multithreaded programmes, GPU computing, computer cluster programming, C++ threads, OpenMP, CUDA, and MPI. By the end of the course, students acquire a deep understanding of high-performance computing principles and techniques. |
MH6544
Bayesian Modelling and Statistical Learning
1.5 AU | This course provides a comprehensive introduction to Bayesian modelling and statistical learning. Students begin by mastering key Bayesian concepts, including prior distributions, likelihood functions, and posterior distributions, learning to build and interpret Bayesian models for various data types. The course then transitions to statistical learning, covering topics such as supervised and unsupervised learning, linear regression, classification methods, and advanced techniques like neural networks and ensemble methods. Practical exercises and case studies are integrated throughout, enabling students to apply these methods to real-world datasets. By the end of the course, students will be equipped to tackle complex problems in machine learning, data science, and decision-making under uncertainty, with a strong foundation in both Bayesian and statistical learning approaches. |
MH6545
Computational Imaging: Methods and Applications
1.5 AU | This course provides a comprehensive exploration of the mathematical foundations underlying various imaging techniques used in medical, seismic, non-destructive testing, and other industrial applications. Students will study key mathematical concepts such as Fourier transforms, wavelets, inverse problems, and optimisation methods, which are crucial for image reconstruction and analysis. In addition to traditional techniques, the course incorporates advanced topics in neural networks, focusing on their application to imaging. Students will explore how neural networks are used to enhance image reconstruction, handle large datasets, and improve image quality across different modalities. Through theoretical lectures and practical exercises, students will develop the skills to model, analyse, and implement both conventional and neural network-based imaging algorithms, enhancing their ability to tackle complex imaging challenges across various fields. |
MH6546
Time Series Analysis and Signal Processing
1.5 AU | This course offers an integrated approach to time series analysis and digital signal processing, appealing to students across diverse fields like engineering, healthcare, finance, and data science. It provides both theoretical foundations and practical applications, covering key topics such as time series modelling, stationarity, autocorrelation, ARIMA models, and forecasting methods. Additionally, students will explore essential signal processing techniques, including the discrete Fourier transform, wavelet transform, and spectral estimation methods. By the end, students will gain skills applicable not only in engineering and healthcare domains but also in finance, for tasks such as stock price forecasting and risk analysis, and in data science for analysing complex datasets and developing predictive models.
Relevant courses at NTU: AI6123 Time Series Analysis offered by MSc in Signal Processing and Machine Learning |
MH6547
Simulation of Physical, Biological, and Chemical Systems
3 AU | This course provides a comprehensive foundation in physical, biological, and chemical sciences, along with their practical applications across various sectors. The physical foundations cover computational mechanics, thermodynamics and heat transfer, and electromagnetics and wave propagation. In the biological domain, the course delves into computational biology for modelling biological systems, systems biology and bioinformatics, and applications in genomics, proteomics, and metabolic networks. The chemical foundations include computational chemistry techniques such as molecular dynamics and quantum chemistry, chemical kinetics and reaction dynamics, and material science with molecular simulations. Additionally, the course addresses interdisciplinary applications in environmental modelling and simulations, biomedical engineering, and chemical engineering processes and simulations. |
MH6548
Modelling and Simulation in Materials Science and Engineering
3 AU | This course explores key modelling and simulation techniques used to predict material behaviour at various scales. Students will learn computational methods like molecular dynamics, finite element analysis, and phase-field modelling to study the mechanical, thermal, and electrical properties of materials. Applications include nanomaterials, polymers, metals, and ceramics. students will develop the skills to perform simulations, interpret results, and apply findings to real-world engineering problems. By the end of the course, students will be equipped with the knowledge to use computational tools effectively in materials research and development.
Relevant courses at NTU: MS7240 Modelling of Materials offered by MSc in Materials Science and Engineering |
MH6549
Modelling and Simulation in Medicine and Healthcare
3 AU | This course focuses on computational techniques to solve challenges in medicine and healthcare. Students will learn methods to simulate biological systems, including tumour growth, cardiovascular function, and disease mechanisms. The course covers systems biology and bioinformatics, with applications in genomics, proteomics, and metabolic networks. It also includes modeling and simulation for biomedical engineering, such as medical devices, physiological processes, and imaging. Techniques like finite element analysis and machine learning for healthcare analytics are introduced. Through practical exercises and projects, students will gain skills to apply these models to real-world medical challenges, preparing them to advance medical research and healthcare innovation.
NTU Relevant courses: MD7103 Biomedical Imaging and
MD7113 Computational Neuroscience by Lee Kong Chian School of Medicine. |
MH6550
Modelling and Simulation in Economics and Finance
3 AU | This course explores modelling and simulation techniques for analysing economic and financial systems. Key topics include stochastic processes, econometric modelling, and financial risk management. Students will learn to apply numerical methods for simulating economic behaviours, asset pricing models, and portfolio optimisation. The course covers Monte Carlo simulations, agent-based modelling, and the use of machine learning algorithms for predictive analytics in finance. Emphasis is placed on practical applications, including market risk assessment, economic forecasting, and financial decision-making.
NTU Relevant courses: MH6831 Quantitative Methods in Finance by MSc in Financial Technology, AE6201 Macroeconometric Modeling and Forecasting by MSc in Applied Economics and Numerical Methods for Financial Instrument Pricing by MSc in Financial Engineering. |
MH6551
Modelling and Simulation in Data Science and Computing Engineering
1.5 AU | This course provides an in-depth exploration of modelling and simulation techniques essential for data science applications. Students will learn advanced methodologies for constructing and analysing predictive models, including statistical modelling, machine learning algorithms, and simulation techniques. The course covers key areas such as data preprocessing, feature selection, and model validation, with a focus on applications in big data, anomaly detection, and optimisation. Students will gain hands-on experience with simulation tools and software, learning to simulate complex data scenarios and validate model performance. Case studies and practical projects will enable students to apply these techniques to real-world data science problems, preparing them to solve challenges in various domains. |
MH6552 - AI-Assisted Independent Study
1.5 AU | This course aims to develop students' ability to undertake independent, AI-assisted study and solve open-ended problems in modelling and simulation. Students will learn how to effectively leverage modern artificial
intelligence (AI) tools to acquire new knowledge, formulate and investigate technical problems, develop mathematical and computational models, perform simulations, analyse and interpret results, and communicate their findings. The course emphasizes the responsible and critical use of AI as an intelligent assistant rather than a substitute for human judgement. Through a self-directed independent study project, students will strengthen their problem-
solving, analytical, communication, and lifelong learning skills, preparing them for careers in research, industry, and other professions that increasingly rely on AI-enabled scientific and engineering workflows. |
MH6553 - Quantum Computing
1.5 AU | Quantum computing is an emerging technology with the potential to transform fields such as cryptography, optimization, artificial intelligence, and scientific simulation. As quantum technologies continue to advance, there is a growing demand for graduates with foundational knowledge in quantum information processing. This course aims to equip you with the central framework and tools which are paramount to understanding the advantage brought by quantum information processing. You will learn a comprehensive overview of the basics of quantum computing and communication. These skills are critical for you who are aiming at a career in quantum information technologies. |
MH6591
Graduate Research Practicum
3 AU | The “Graduate Research practicum” is a key component of the programme supervised by a faculty advisor, with weekly consultations lasting at least 4 hours per week. These research projects are designed to immerse students in advanced concepts and skills relevant to modelling and simulation. Projects can be conducted in university laboratories, local research institutes, or approved industrial sites for supervised industrial projects. The assessment will focus on research performance evaluated by the project supervisor, as well as project report and oral presentation assessed by appointed examiners. Students are required to select either MH6591 or MH6592 or may choose not to enrol in either course. |
MH6592
Graduate Professional Internship Practicum
3 AU | This is a 10-week internship training at local industries with project scopes relevant to modelling and simulation. Internship projects will be primarily arranged by the School’s programme management team, although self-sourced projects are permitted pending approval from the academic Programme Director. The internship performance will be jointly evaluated by the organization supervisor and an appointed faculty supervisor. Assessment criteria include work performance (50%, evaluated by ), project supervisor), project report (25%, evaluated by the faculty supervisor), and oral presentation (25%, evaluated by the faculty supervisor). The faculty supervisor will conduct at least one site visit to discuss the progress of the student's work with the organization supervisor and to moderate the assessment of work performance. The outcome of the internship assessment will be graded. Students are required to select either MH6591 or MH6592 or may choose not to enrol in either course. |
