SMP Winter Research Projects

Hours of engagement & delivery mode: 32 hours per week, 4 weeks (29 June–24 July 2026). The project can be done on campus at St Lucia, or online, or a combination of these. Students will need to be available for meetings in person or via Zoom.

Description: Brownian motion is often viewed as an unfortunate source of uncertainty in measurements using optical tweezers. When nanoparticles are trapped, the greater speed of by the particles through Brownian motion can enable their use as volume-exploring probes. One approach to the dynamics of Brownian motion in a trap is to solve the diffusion equation in the presence of a potential—a single trapped particle behaves as an ideal gas, due to the absence of inter-particle interactions. You will solve the steady-state and time-dependent diffusion equation modelling the trap as (a) a conservative potential and (b) also with the inclusion of non-conservative forces. You will use these solutions to explore applications of nanoparticles as Brownian-motion-exploiting probes.

Expected learning outcomes and deliverables: You will gain experience in computational solution of PDEs, and the use of mathematical and computational methods in physics. You will also gain experience in scientific communication.

The outcomes of a successful project will be:

• Software implementing a solution of the diffusion equation relevant to the optical trapping on nanoparticles.
• A written report presenting your results.

Suitable for: This project will suit students interested in optical trapping and computational physics. Applicants should have experience in numerical methods and PDEs. Second level course in physics and maths, and/or COSC2500 should provide a suitable background.

Primary Supervisor: Dr Timo Nieminen

Further info: Students can contact the primary supervisor for more information at timo@physics.uq.edu.au.

Hours of engagement & delivery mode: 20 hours per week on St Lucia campus (remotely from 29 June to 3 July and from 15 July  to 17 July).

Description: Stochastic processes describe systems that evolve randomly over time, and their theory is widely used in engineering, finance, insurance, and operations research.

This project focuses on a particular class of stochastic processes known as Lévy processes, which can be viewed as continuous-time extensions of random walks. Important examples include Brownian motion and the Poisson process. Lévy-type models are well suited to systems that evolve continuously but may also experience sudden jumps, providing natural frameworks for describing, for example, the surplus of an insurance company subject to random claims, or the workload and congestion levels in queueing systems.

The main aim of the project is to develop analytical and computational methods for evaluating key performance measures, such as the probability of bankruptcy (ruin probability) of an insurance company and the probability of congestion or overflow in queueing systems.

Expected learning outcomes and deliverables: YStudents will develop a solid understanding of continuous-time stochastic process models, learn how to formulate and analyse probabilistic models arising from real-world systems, and apply both analytical and computational methods to evaluate key quantities of interest.

Suitable for: 3^rd and 4^th year Mathematics students, having completed STAT2003 and preferably advanced probability/analysis courses.

Primary Supervisor: Dr Kazutoshi Yamazaki

Further info: Please contact Dr Kazutoshi Yamazaki at k.yamazaki@uq.edu.au for further information.. 

Hours of engagement & delivery mode: Project is mainly on-site in St Lucia and involves experimental testing in the research laboratory (located in the basement of Parnell building).  This is a 4-week project. The time is flexible, but the student is expected to work 24 hours per week.   

Description: Temperature measurement on nanoscale is challenging. Different sensors are proposed for this task. In this project the student will work with nanodiamonds with imbedded nitrogen-vacancy (NV)centres. Such measurements exploit double-resonance technique, a combination of microwaves and visible light which enable detection of spin resonances in NV-centres. 

Expected learning outcomes and deliverables: The student gain skills in programming computer control of experimental setup, working with optical and microwave devices, photon detection with ultra-sensitive camera.  The goal of this project is to improve the. Readout rate and reduce the noise in the signal caused by low frequency fluctuations.

Suitable for: The project is suitable for students with background in IT and good programming skills (or strong motivation for learning)  and basic knowledge of physics (at the level of second year QM and Fields or above). 

Primary Supervisor: Associate Professor Taras Plakhotnik

Further info: Students may contact the supervisor by email at taras@physics.uq.edu.au for additional information about the project.

Hours of engagement & delivery mode: 20-36 hours per week over 4 weeks. St Lucia Campus (in person, online, or hybrid).

Description: The goal in this project is to model data resulting from tracking of protein positions in the active site of neuron cells. We will formulate stochastic processes as mathematical models for how proteins translocate in heterogeneous environments characterised by the presence of scaffolding proteins.

We will use Bayesian Inference (Computational Statistics) to infer parameters from synthetic and experimental data. As this is a short (winter) project, the focus will be on coming up with a proof of concept that works (runs) at least in a simplified setting. It will be also required to write a report on this which can serve as basis for further work on this project.

Expected learning outcomes and deliverables: Skills in computational statistics, parameter identification, agent-based modelling, academic writing.

Suitable for: Open to applications from students with background in mathematics/statistics, 3-4th year students.

Primary Supervisor: Dr Dietmar Oelz

Further info: Please contact me at d.oelz@uq.edu.au, if you are interested in this winter project.

Hours of engagement & delivery mode: 24–30 hours per week (within official program dates). Primarily in-person at St Lucia (with optional remote computational components)

Description: Light does more than illuminate biological systems for imaging: it can push, trap, rotate, organise, and control them.

This project explores how structured optical fields (including tightly focused laser beams and vortex beams) can be used to manipulate microscopic biological particles such as bacteria, algae, or synthetic microswimmers. Using tools from optical trapping and non-equilibrium physics, we investigate how external optical forces compete with thermal noise and biological activity.

At microscopic scales, the world is dominated by fluctuations. Particles do not move smoothly. They jitter, escape, reorient, and respond to both random forces and externally imposed fields. Understanding this interplay is essential for:

• Controlling microorganisms in microfluidic environments
• Probing mechanical properties of cells
• Studying collective motion in active matter systems
• Designing optical tools for biomedical applications

The student will contribute to a focused sub-project that may include:

• Modelling optical trapping potentials and escape dynamics
• Simulating active particles under optical forces
• Exploring how structured light (e.g., beams carrying angular momentum) can induce rotation or organisation
• Quantifying stability and confinement in noisy environments

Depending on background, the project may involve computational modelling, data analysis of experimental measurements, or theoretical derivations of trapping stability.

This work links modern photonics with biological physics and represents a fast-growing research direction with real potential impacts on future technology.

Expected learning outcomes and deliverables: 

By the end of the project, students will:

• Understand the physical principles behind optical trapping and radiation pressure
• Learn how microscopic particles behave in noisy, non-equilibrium environments
• Develop computational models of particle dynamics under optical forces
• Gain insight into how structured light can impart torque and control motion
• Learn how to connect physical models to biological applications

Deliverables may include:

• A structured research report (5–10 pages)
• Reproducible simulation or analysis code
• A final oral presentation to the research group
• (For outstanding projects) the possibility of contributing to ongoing experimental work or a future publication

Students will receive mentoring in experimental reasoning, modelling, and scientific communication.

Suitable for: 

This project is ideal for motivated undergraduate students in:

• Physics
• Engineering
• Applied Mathematics
• Photonics
• Biophysics

Applicants should have:

• Strong mathematical foundations
• Some exposure to electromagnetism or classical mechanics
• Programming experience (Python or MATLAB preferred)
• Curiosity about how physics tools can directly control biological systems

​​​​​​​Third- and fourth-year students are strongly encouraged to apply. However, I’m happy to talk with anyone!

Primary Supervisor: Dr Alexander Stilgoe

Further info: Students are encouraged to contact Dr Stilgoe at stilgoe@physics.uq.edu.au prior to applying to discuss their interests and suitability.

Hours of engagement & delivery mode: The hours of engagement would be up to 30 hours per week for 4 weeks. The project will be in person at St Lucia campus.

Description: This project aims to optimise existing protocols for synthesising and coating vaterite microspheres, while also introducing quantum-sensing capabilities. This will be achieved by integrating nitrogen-vacancy nanodiamonds into the vaterite synthesis pathway of vaterites to produce a novel probe particle that combines mechanical and quantum measurements techniques. The performance of these probes will be quantified using fluorescence spectroscopy, ODMR, and rotational optical tweezers experiments to evaluate the sensing capabilities and assess their optical properties for trapping purposes.

Expected learning outcomes and deliverables: 

Scholars can expect to develop skills in data collection, data analysis, and practical laboratory techniques that span both physics and chemistry. There is also potential to contribute to publications related to the project. At the end of their project, Scholars are expected to produce a written report of their research and deliver a short presentation to the research group summarising their progress.

Suitable for: This project is well‑suited to second‑ and third‑year students seeking hands‑on laboratory experience. Ideally, applicants will have completed first‑year chemistry and have an interest in combining classical optical trapping techniques with quantum‑sensing nanoparticles.

Primary Supervisor: Dr Mark Watson

Further info: If you are interested in this project, please contact Mark Watson at mark.watson@uq.edu.au to discuss it further.

Hours of engagement & delivery mode: 32 hours per week, 4 weeks (29 June–24 July 2026). The project can be done on campus at St Lucia, or online, or a combination of these. Students will need to be available for meetings in person or via Zoom.

Description: Material with exotic optical properties such as negative refractive indices, zero or near-zero refractive indices, etc. can be made using metamaterials. Such particles might offer novel behaviour in optical traps such as optical tweezers. You will explore the behaviour of a range of exotic particles in optical tweezers, including negative index, zero-index, near-zero-index, refractive-index matched, and impedance-matched particles. Your main tool for this will be out Optical Tweezers Toolbox: https://github.com/ilent2/ott. 

Expected learning outcomes and deliverables: You will gain experience in computational optics and physics, and the theory of exotic optical materials. You will also gain experience in scientific communication.

The outcomes of a successful project will be:

• A written report presenting your results.
• Extend our OTT to include arbitrary combinations of permittivity and permeability.

Suitable for: This project will suit students interested in optical trapping and computational physics. Applicants should have experience in Matlab and  numerical methods, and the theory of optics. The second level fields course in physics (PHYS2055 Fields in Physics I) should provide a suitable theoretical background.

Primary Supervisor: Dr Timo Nieminen

Further info: Students can contact the primary supervisor for more information at timo@physics.uq.edu.au.

Hours of engagement & delivery mode: 30 hours per week on St Lucia campus.

Description: Parasitology has a long tradition as a classical testing ground for applied‑probability modelling, especially through stochastic processes that describe parasite acquisition and loss. In a standard model of parasite acquisition, it is known that density‑dependent parasite mortality produces parasite-load distributions for which the variance-to-mean ratio is less than one. However, it is not clear how density‑dependent parasite death influences other commonly used measures of parasite aggregation, such as those based on the Lorenz curve.

This project aims to determine whether density‑dependent mortality consistently reduces aggregation across a broad range of metrics, or whether different measures reveal qualitatively distinct patterns. Although both numerical experiments and detailed analysis may be used, the balance between the two approaches can be adjusted depending on the student’s background and interests.

Expected learning outcomes and deliverables: Students will gain an understanding of how probability modelling contributes to parasitology, emphasising its value in describing the distributional patterns that arise in real systems. Through working with Markov chain models, students will extend their existing knowledge of stochastic processes, while also being introduced to additional concepts such as stochastic ordering.

The student will submit a short report that explains the methodology they have used, presents the results of their investigation, and interprets these findings within the broader context of probabilistic modelling in parasitology.

Suitable for: Mathematics students interested in applied probability. Having completed STAT3004 would be an advantage.

Primary Supervisor: Dr Ross McVinish

Further info: Please contact Ross McVinish at r.mcvinish@uq.edu.au for further information.

Hours of engagement & delivery mode: 20-36 hrs per week over 4 weeks. Hybrid arrangement with bi-weekly meetings.

Description: Quantum computing promises to solve computational problems beyond the reach of classical supercomputers. A candidate hardware platform is superconducting qubits which are being developed at UQ (Federov group) and in industry (e.g. IBM and Google). The performance of these near-term devices are typically limited by noise. In particular, performing measurements on quantum bits (qubits) is seen to be orders of magnitude worse than performing logical quantum gate operators. In this project, we will use ARIMA techniques to learn temporal correlations in measurement data to improve measurement fidelities on superconducting qubits. We will compare with existing benchmarks using matched filtering and kernel methods. 

Expected learning outcomes and deliverables:  

• Understand basic quantum computing and noise in superconducting qubit measurements
• Produce software using python programming
• Develop and test statistical regression models such as vector-ARIMA
• Develop and test to ML methods such as kernels to classify trajectories of quantum measurements

Suitable for: 

• Demonstrated track record in python software programming
• Undergraduate background in linear algebra (at least 2^nd year)
• Any experience in kernel methods, ARMA models, filtering algorithms, quantum computing, or quantum mechanics highly desirable

Primary Supervisor: Dr Riddhi Gupta

Further info: Please contact Dr Riddhi Gupta: riddhi.gupta@uq.edu.au.