Courses Offered (AY2026/2027)

This is a new course which aims to highlight the relevance and importance of physics in many aspects of technology. It aims to serve as the overview course to expose the students to a few key technological development when Physics plays a vital role. This course will be conducted by our own lecturers. The selected topics will be current and directly relevant to the potential career options that the MSc students will be considering. Discussion of each topic shall cover the basic physics principles leading to the state of the art development in the technology. The duration on each topic can last from 2 weeks to 3 weeks. Examples of the topics include energy and batteries, solar energy systems, quantum technologies, computer modelling in Physics, sensor devices, communication systems, microelectronics, advanced functional materials, biophysical instruments, etc.

This course covers a series of lecture clusters/seminars in industrial physics co-taught by our lecturers and our industrial partners and collaborators. Students will be exposed to the multiple-faceted career options that a physicist can choose in the industry. Our industrial partners will provide an overview of a certain industry sector and share their experience on the role a physicist plays in this sector. Our partners shall also emphasize the important skillsets to learn in order to be well-prepared for the career chosen. The range of industrial sectors shall cover Semiconductors, Engineering, Material Science, IT, Data Sciences, Energy Sector etc.

This is a required course for all research Masters and PhD students admitted from AY2004/2005. The main purpose of this course is to help graduate students to improve their presentation skills and to participate in scientific seminars/exchanges in a professional manner. The activities of this course include giving presentations during the lecture hours and attending seminars organised by the Department. Students are also required to write summaries of some departmental seminars attended. The grade of this course will be “Satisfactory/Unsatisfactory” based on student’s talk presentations, participation of seminars and the summary writing.

This course is an introduction to advanced topics in quantum mechanics. Topics include relativistic quantum mechanics and the Dirac equation, identical particles and second quantization, and the path-integral formulation of quantum mechanics.

This course presents an introduction to phase transitions and fluctuations. For phase transitions, the course starts with the treatment of Landau and mean field. Exact Ising model results are then discussed. Critical exponents are introduced and their relations obtained using the scaling hypothesis and Kadanoff’s scheme. Real space renormalization is then used to show how the critical exponents can be calculated. For fluctuations, Langevin, Fokker-Planck equations will be used. Time dependence and fluctuation dissipation theorem then follow. Brownian motion will be used as an example. This course is targeted at physics graduate students with at least one year of statistical mechanics.

This course aims to give graduate students additional training in the foundations of solid state physics and is intended to prepare them for research work and other graduate coursework courses. Topics to be covered include: translational symmetry and Bloch’s theorem, rotational symmetry and group representation, electron-electron interaction and Hartree-Fock equations, APW, OPW, pseudopotential and LCAO schemes of energy band calculations, Boltzmann equation and thermoelectric phenomena, optical properties of semiconductors, insulators and metals, origin of ferromagnetism, models of Heisenberg, Stoner and Hubbard, Kondo effect. Students are expected to read from a range of recommended and reference texts, and will be given an opportunity to present their reading as part of the regular lessons.

Spintronics is the study of the intrinsic spin of the electron and its application in spin-logic and devices, spin-polarized injection devices, and storage media. This is important for a variety of current and emerging applications in magnetic memories. This course equips students with essential knowledge of magnetism, and exchange interactions in solids; half metals, and dilute magnetic semiconductors; spin injection, transport and detection; and magnetic nanostructures, and their applications in: GMR read-write heads, MRAM, spinFET, spin-torque oscillators.

This course provides an introduction to surface physics for graduate and year-4 undergraduate students major in physics, chemistry and materials science and engineering. It covers the properties of solid surfaces, experimental techniques and applications. The topics include the importance of surfaces in science & technology, surface crystallography and topography, surface energy and stress, surface electronic properties (surface states, work function, band bending and Fermi level pining at semiconductor surface/interface, magnetism), surface phonon and plasmon, adsorption, desorption and reaction on surfaces. The applications of basic surface science knowledge in semiconductor technology, materials growth and processing, heterogeneous catalysis, nanoscience and thin film technology will be demonstrated. Experimental techniques, such as XPS, UPS, AES, LEED, STM, AFM, SIMS, EELS, TPD and vacuum technology, will be addressed with examples and applications. To take this course, students should have a basic knowledge of quantum physics, thermodynamics and solid state physics.

This is a course on quantum field theory aimed at students who have had some exposure to relativistic quantum mechanics. The topics covered are: canonical quantization and the path-integral formulation of quantum field theory, Feynman rules for scalar, spinor, and vector fields, regularization and renormalization, and the renormalization group.

The course aims to provide a comprehensive understanding on the principles of nonlinear optics. The course is targeted at postgraduate students who have acquired a background in optics, and who are involved in optics-related studies and research. The course presents the principles of nonlinear optics and photonics devices, which includes: nonlinear optical susceptibility, wave propagation in nonlinear media; sum and difference frequency generation, parametric amplification and oscillation, photonic crystals; phase conjugation, optical-induced birefringence, self-focusing, nonlinear optical absorption, photonic devices; ultrafast laser.

This advanced course presents the fundamentals of classical electrodynamics in much depth. It covers the following topics: Maxwell’s equations to define conservations laws for energy and momentum, properties of electromagnetic waves, light scattering from interfaces, the concept of optical dispersion, and investigate how waves propagate in bounded structures such as waveguides and transmission lines. In depth investigations of radiation by moving charges, special relativity from Maxwell’s equations such as relativistic length contraction, and covariant formulation of E&M. An understand of applications ranging satellites to fiber optics to transmon qubit would be related to E&M. A good mathematical foundation is required.

This course provides an introduction to the physics of nanostructures. Students taking this course will be introduced methods of fabrication and characterization of nanostructured materials and nanodevices, common types of nanostructures, their properties and applications. More importantly, the underlying physics of the intricate properties and functions of nanostructures will be discussed. The course starts with a brief review of relevant topics of quantum mechanics and solid state physics in reduced dimensions. Common techniques for nanostructure fabrication and characterization are introduced next. Transport in low-dimensional systems, optoelectronics of nanostructures, nanotubes and nanowires, clusters and nanocrystallites are discussed. Finally, magnetic nanostructures, and molecular electronics (optional), will be covered. This course is designed for postgraduate students who are interested in nanoscience and nanotechnology research and applications. Understanding the physics of nanostructures will allow the students to better appreciate the interesting properties and their tunability of nanostructures, understand the operating principles of nanodevices, and to design and optimize nanostructures for different applications.

This course discusses the molecules in cells and the physics behind their functions. At the core is the understanding of biomolecular conformations, structural stability and interactions under physical constraints such as force, geometry and temperature, by theory and state-of-art experimental technologies. Besides homework and quiz, projects are an important component of assignments. Multiple projects are provided for students to choose, which may involve numerical/Monte Carlo simulation of biomolecular conformations, analysis of experimental data, or investigation of the DNA micromechanics by analyzing DNA conformations. This course is targeted at students who have a basic knowledge in general physics and thermodynamics.

The ability to setup high-quality experiments and measurements is fundamental to innovation in many areas of sciences and engineering, including materials and devices. Therefore a good understanding of, and practical training, in experimental physics techniques is essential to a lot of research and development work in both academia and industry. This course equips students with the essential knowledge and practical skills in a broad range of modern experimental physics techniques, including: mechanical design and materials selection; vacuum technology, cyostats, and thin-film deposition techniques; Gaussian beam laser optics; photodetectors; stepper motors and piezoelectric actuators; feedback and control loops; techniques in analog, digital and pulse signal processing; weak-signal detection and lock-in amplifiers; fast-signal detection and transmission lines. The practical skills will be taught in laboratory classes, which are part of this course.

Covers computational techniques for the solution of problems arising in physics and engineering, with an emphasis on molecular simulation and modelling. Topics will be from the text, “Numerical Recipes”, Press et al, supplemented with examples in materials and condensed matter physics. This course insures that graduate students intending to do research in computational physics will have sufficient background in computational methods and programming experience.

This course introduces from an experimentalists point of view to the modern world of ultracold quantum gases that so much changed atomic physics in the past two decades. The lectures present the basic experimental methods of laser cooling, magnetic and optical trapping, and evaporative cooling that produce matter near absolute zero temperature. We then discuss basic effects like Bose-Einstein condensation and Pauli pressure. Further, selected research examples are presented that give insight to some of the many close relations between quantum matter designed in many labs worldwide and other physical systems found in the range of quantum information science, condensed matter physics, metrology, nuclear physics, and astronomy. Solid background in quantum mechanics, atomic physics, and statistical mechanics is desired.

This course will introduce a phenomenological description of superconducting materials and their applications to modern technologies. For this, the course will cover bulk and thin-film superconducting materials and introduce the Josephson junction, which is the basis of many superconducting devices. From this, we will introduce the main parameters that are relevant to the design of modern superconducting devices, namely resonators, qubits, SQUIDs and photodetectors. Finally, we will cover how the choice of materials and geometry influences the functioning of these devices.

This course will introduce modern theoretical concepts and methods of quantum many-body physics. It will cover tensor networks, a graphical framework for manipulating and classifying quantum many-body states based on quantum entanglement. It will discuss bounds on quantum information propagation, and how they constrain the behavior of correlation functions of phases of matter. It will also introduce quantum circuit models as testbeds to probe collective dynamical phenomena like thermalization and emergence of random matrix theory. This course is relevant for understanding and describing the novel physical regimes realized by emerging quantum simulator and quantum computational technologies.

The course provides an introduction to quantum information and quantum computation. In addition to physics majors, the course addresses students with a good background in discrete mathematics or computer science.The following topics will be covered: (1) Introduction: a brief review of basic notions of information science (Shannon entropy, channel capacity) and of basic quantum kinematics with emphasis on the description of multi-qubit systems and their discrete dynamics. (2) Quantum information: Entanglement and its numerical measures, separability of multi-partite states, quantum channels, standard protocols for quantum cryptography and entanglement purification, physical implementations. And (3) Quantum computation: single-qubit gates, two-qubit gates and their physical realization in optical networks, ion traps, quantum dots, Universality theorem, quantum networks and their design, simple quantum algorithms (Jozsa-Deutsch decision algorithm, Grover search algorithm, Shor factorization algorithm). The course is tightly integrated with IBM quantum computer hands-on experience via IBM Q Experience cloud services. Students will learn fundamentals of Qiskit, a modern and rapidly developing quantum computer programming language, by directly implementing concepts learnt in the classroom.

Functional electronic devices are an essential part of modern technology, and they are used in a wide range of applications, including communication systems, computers, medical devices, and consumer electronics. In this course, we will discuss the working principles of a variety of functional electronic devices, such as transistors, diodes, and different photodetectors. We will focus on the physical concepts behind their work and how those devices can be built and/or improved using novel artificial materials such as van der Waals heterostructures and 2D materials.

When in operations, functional electronic materials are often driven out of equilibrium by either external bias voltages or thermal energies. In this course, fundamental computational theories for modelling different types of nonequilibrium materials and their applications in electronics, thermoelectric effects and catalysis will be discussed. Basic computational techniques for modelling nonequilibrium materials will be taught. Students have opportunities to practice these techniques through projects.

This course exposes graduate students to examples of Machine Learning and Data Science that are commonly encountered in data analyses in the Physical Sciences (e.g. optics, statistical physics, condensed matter, biological physics). We will take a hands-on approach to implementing, training, and evaluating machine learning models. This course will be taught in the Python programming language. Prior experience in any programming language will be helpful.

In the age of big scientific data, Bayesian statistical methods and machine-learning techniques are becoming a vital part of the modern scientist’s toolkit. This course provides a graduate-level introduction to the two related fields, with equal emphasis on both. Key topics for the first part include: fundamentals of probability and inference, hierarchical modelling, model validation and comparison, and Monte Carlo methods; for the second part, they include: classification and regression, kernel methods, variational methods, and neural networks. The course will be largely theoretically oriented, with the occasional computational component.

Much of our real world data are manifestations or measurements of their underlying complex interactions. Hence, modelling and analysis of the underlying complex systems can reveal understandings and predictions that complement black-box machine learning tools. This course will cover the basic concepts and tools in analysing complex systems and simulation models, and more importantly why and when we need such white-box tools derived from statistical physics. Certain key concepts in complexity science will be intrudcued. It will also provide hands-on experience with system analysis and imulation modelling in Python.

This course covers the physical principles behind a wide variety of nano/micromachines and active matters involving these small energy-consuming building blocks. Specifically, the course covers molecular motors, nano/micro-robots, microswimmers, related active matters, and applications (e.g., actuation, precise control, chemistry, biotechnology, precision medicine, etc.). This course aims at a unified physical understanding, mainly based on stochastic thermodynamics, fluid dynamics at low Reynolds numbers, and active soft matter theories. The course focuses on artificial systems but also touches biological counterparts. Advanced design and fabrication methods like DNA nanotechnology will be discussed too.

In this course, the physics behind a wide spectrum of modern sensors is covered, capturing basic properties like temperature, distance, forces, pressure, magnetic fields, and light that are relevant in everyday applications, as well as more advanced sensors for acceleration and rotation that became commonplace in mobile devices for orientation and navigation. Furthermore, advanced sensing techniques used in microbalances, particle detection and advanced optical and acoustic sensing techniques will be discussed.

This course introduces the basic building blocks for the theory of quantum measurements. With this detailed knowledge, a rigorous discussion of measurement models, the von Neumann model in particular, error-disturbance relations, incompatibility of measurements, and sequential measurements becomes possible. During the introduction of these concepts, the students will also acquire knowledge in operational quantum theory as well as become fluent in the mathematical framework of Hilbert space quantum mechanics.

After this course, the students will master and be knowledgeable in the mathematics and operational meaning of the basic concepts of quantum measurements. This knowledge will make them able to understand key quantum physical concepts like uncertainty and error and disturbance in measurements.

Analysis of Boolean functions has over the years provided important tools that deepened our understanding and simplified the analysis of fundamental concepts in computer science. From the theory of voting to machine learning to pseudorandom generators, Boolean functions provide both technical tools and intuitive insight.

In this classical course, we will give an introduction to these tools, and the corresponding applications.

The Course covers many important topics in modern quantum information theory, including error correction and fault tolerance, quantum algorithms, entanglement and communication theory, as well as nonlocality and quantum cryptography.

This Course will equip the students with an interest in theoretical aspects of quantum information with the basic tools and concepts required to do research in the field.

The Course introduces contemporary physical hardware systems that form the basis for processing quantum information with actual devices. An overview will be presented in several lecturers, and specialized topics covered in small seminar-style presentations by students.

The Course should equip students from a broad background with a basic understanding of the working mechanism of various physical approaches at an overview level and inform about relevant strengths and challenges for the specific systems.

In this course, various experimental techniques for manipulation of quantum systems will be explored, partly through introductory lectures ,partly through small hands-on projects. The topics tentatively covered are frequency control of laser systems, homodyne detection techniques, generation of pulse sequences, high voltage techniques, electro-optics, accousto-optics, and liquid crystals for light modulation, basic optical fiber technology, optical cavities paraxial optics, and practical aspects of superconducting systems.

Machine learning (ML), in most scientific applications, is a computational model of statistics. This course will give students a broad overview of why and how ML is used in the sciences. It will also provide some context for how ML tools were developed to solve certain classes of challenges, highlighting the unique requirements of ML in science. For example, the use of ML in curiosity-driven exploration, understanding how the data was prepared and labelled, determining (where possible) whether the use of ML was ill-posed or ill-conditioned, interpreting the predictions of these ML models, and turning these into hypotheses.

This course exposes graduate students to the computational basics of Machine Learning and Data Science commonly used for exploration and discovery in the sciences (e.g., optics, statistical physics, condensed matter physics, structural biology, chemistry, materials science, and epidemiology). We will take a hands-on approach to building, implementing, training, and evaluating machine learning models (in Python), through examples of discovery and exploration in scientific applications.

This course presents the mathematical and computational foundations of machine learning, preparing the students with sufficient background for more advanced topics such as natural language processing. The learning outcomes include adequate familiarity with the programming environment for machine learning with Python, a deeper understanding of the building blocks of neural networks, and numerical training algorithms for machine learning. The course will draw science applications to illustrate deep learning concepts.

This course will be based on a series of seminars by leading AI practitioners in Singapore and overseas, both in academia and industry. The idea is to expose students to rapidly changing trends in AI and breakthroughs in applying ML to existing and future problems. Doing so will help students appreciate how tangible tools they learned in other courses in this programme can impact research. Students will think critically about and discuss the content of these seminars with their peers. Students will also write short summaries of these seminars that will be assessed.

Astronomy and astrophysics are transforming in the era of Big Data, with massive datasets of celestial phenomena collected by next-generation telescopes. This course explores the emerging role of AI in accelerating and enhancing the scientific analysis of these astronomical datasets, facilitating discoveries, and addressing fundamental questions about our Universe. Students will gain insight into how AI optimizes and improves upon modern techniques utilized in astronomy, with key topics including the data mining of astrophysical measurements, time-domain analyses, spectral analysis techniques, and probabilistic inference. Through lectures and hands-on projects, students will explore observations of a variety of celestial objects, including exoplanets, variable stars, and galaxies. They will develop an understanding of the science underlying celestial phenomena and form a problem-solving mindset utilizing AI, focused on tackling intriguing problems in astronomy and astrophysics.

Imaging has become highly data-driven and reliant on computation. New imaging modalities have emerged that incorporate known principles of optics (e.g., beam shaping, propagation), beam-matter interactions, and priors about the sample. These have resulted in a new form of imaging known as computational lenses, which does far more than what physical lenses alone can accomplish. The development of computational lenses is accelerated by machine learning, affordable and fast computing, and high-throughput data collection. This course will equip students with the foundations needed to engage and extend imaging empowered by machine learning. We start by building up the essential optics foundations in imaging and practical aspects of detection physics. Then, students will learn about computational methods (both conventional and machine learning) used in analysing raw images, for instance, separation of signal from noise in detected images, computational phase retrieval, de-noising, computed tomography, segmentation, etc. Finally, we will explore how deep learning is changing this landscape. Throughout the course, students will see how these concepts and tools are being applied in imaging examples in biology.

Machine Learning is rapidly becoming one of the most exciting and useful areas of modern research with important applications across the sciences. This class will introduce the fundamental concepts and applied tools of machine learning while being aligned with the needs and experience of condensed matter physics. We will focus on deep neural networks that can be trained to perform a wide variety of tasks including image recognition, pattern identification, and natural language processing and discuss how these basic techniques can be applied to problems in condensed matter physics, ranging from the prediction of material properties, super-resolution imaging, the analysis of high-dimensional data sets, and to the discovery of new phases.

This course offers students a comprehensive understanding of the fundamental principles of optics and photonics, alongside AI techniques that are transforming this field. It covers electromagnetic modelling, key applications of optics, and AI-driven advancements in optical and photonic technologies. Through case studies on integrated optical circuits and metasurfaces, students will explore AI-driven shape and topology optimization for optical design and discovery. By combining theoretical knowledge with hands-on coding exercises, students will develop expertise in using AI to advance modern photonic technologies.