UNSW Science Vacation Research Scholarship UGVC1056
2022/2023 Research Projects

The purpose of the UNSW Science Summer Vacation Research Scholarship is to expose highly talented undergraduate students, enrolled in Science or a related discipline, to scientific research and other science-based experience, and to further their education and inspire them to consider research or related activities. The scholarship program will run for a six week period over the Summer Semester (November to February).

 

How to apply

To apply you will need to submit the following:

Supporting Documentation

Please submit the following with your Scholarship application:

  • An electronic copy of your CV
  • An electronic copy of your academic transcript

Applications are now open and will close Friday 16 September 2022.

Research Projects

Click on the School to view possible research projects:

School of Biotechnology and Biomolecular Sciences Projects

Project Title: The role of immune cells in regulating thermogenic beige fat

 

Supervisor(s): A/Prof Kate Quinlan

Description: Obesity is a global health problem.  White fat cells, which play a  major role in scoring excess energy, can be converted into beige fat cells which burn fat as heat rather than storing it.  We are interested in determining how beige fat cells can be activated such that adipose tissue could be converted from an energy storing depot to an energy burning factory as a new therapeutic option for obesity.  We are studying the role that adipose-resident immune cells play in beige fat activation by signalling to the fat cells.  This project seeks to shed light on this question using a comination of molecular biology and cell biology techniques.  It is a wet lab project.

Experience: No research experience is required but this project would most suit students who have enjoyed undergraduate subjects in biochemistry, molecular biology, cell biology, genetics and/or immunology. This project is ideally suited to students who are commencing their final year of undergraduate studies in 2023.  This project is not suitable for students who will have completed the final year of their undergraduate degree and are intending to commence honours in Term 1/Semester 1 2023.  The 6-week project can be conducted at a mutually convenient time within the November 2022-February 2023 window with the exception of 19 December 2022 - 16 January 2023.

 

Project Title: Therapeutic reversal of haemoglobin switching 

Supervisor(s): A/Prof Kate Quinlan

Description: Sickle cell disease is a debilitating blood disease that arise due to mutations in adult globin genes. However, humans express different globin genes at different developmental stages.  The foetal globin genes are normally silenced shortly after birth by a gene regulation process known as globin switching.  Some individuals have rare beneficial mutations that allow them to continue to express foetal globin into adulthood and that alleviate the symptoms of sickle cell disease. This project seeks to determine ways that foetal globin silencing can be reversed with the aim of developing new therapeutic options for sickle cell disease. The project uses a combination of molecular biology and cell biology approaches.  It is a wet lab project.

Experience: No research experience is required but this project would most suit students who have enjoyed undergraduate subjects in biochemistry, molecular biology, cell biology and/or genetics. This project is ideally suited to students who are commencing their final year of undergraduate studies in 2023.  This project is not suitable for students who will have completed the final year of their undergraduate degree and are intending to commence honours in Term 1/Semester 1 2023.  The 6-week project can be conducted at a mutually convenient time within the November 2022-February 2023 window with the exception of 19 December 2022 - 16 January 2023.

 

Project Title: Functional characterization of alternative promoters using Perturb-Seq

Supervisor(s): A/Prof Robert Weatheritt

 

Description: Although each cell shares the same genetic information, the different layers of gene regulation orchestrate the diversity and regulation of gene expression which will lead a cell to differentiate. Along with alternative splicing and alternative transcription, alternative promoter usage (APU) is an important mechanism for transcriptome diversity and the regulation of gene expression. Indeed, this alternative usage may influence tissue/subcellular specificity, protein translation, and the function of the proteins.  In this project, we aim to use Perturb-seq to characterize APU. This technology uses CRISPR interference (CRISPRi) coupled with single-cell RNA-seq. The advent of this single cell technology enables a high-throughput measurement of the direct genotype-phenotype relationship in an alternative promoter knockdown.   You will be involved in the validation and functional characterisation of APU genes found in the Perturb-seq assay. Some of the methods you will learn include cell culture, RT-PCR, and Western blot.

Experience: Prior knowledge/experience required for this project: All applicants should have a strong interest in molecular biology, and a curious mind. Science or medical science students with a background in biochemistry, and molecular biology (2nd and 3rd-year level).

 

 

Project Title: Deep Omics!

Supervisor(s): Dr Fatemeh Vafaee

Description: Deep learning has revolutionized research in image processing and speech recognition and will soon transform research in molecular biomedicine. Deep learning models can capture multiple levels of representation directly from raw data without the need to carefully engineer features based on fine-tuned algorithmic approaches or domain expertise. Omics data is one of the most prominent examples of feature‐rich and high‐dimensional heterogeneous data and thus multi-omics data analysis and integration have increasingly become a deep learning harvesting field in computational biology. We are developing deep learning and other machine learning models to leverage large omics data for finding hidden structures within them, for integrating heterogeneous data and for making accurate predictions in different biomedical applications ranging from single-cell omics analysis and multi-omics biomarker discovery to human functional genomics and drug discovery (papers to look into: http://dx.doi.org/10.1093/nar/gkac436; http://dx.doi.org/10.1093/bib/bbab304; http://dx.doi.org/10.1101/2022.05.10.491423).

Experience: Students need to have experience in programming and interest in machine learning/artificial intelligence methods applied to biomedical applications

 

Project Title: Data-driven Drug Discovery

Supervisor(s): Dr Fatemeh Vafaee

Description: Repositioning existing drugs for new indications is an innovative drug discovery strategy offering the possibility of reduced cost, time and risk as several phases of de-novo drug discovery can be bypassed for repositioning candidates. Biopharmaceutical companies have recognised advantages of repositioning, and investment in the area is dramatically increasing. With the rapid advancement of high-throughput technologies and the explosion of various biological and medical data, computational drug repositioning has become an increasingly powerful approach to systematically identify potential repositioning candidates. My lab is running multiple research projects advancing the field of computational drug repositioning. We are developing computational tools and databases which integrate massive amounts of biological, pharmacological, and biomedical information related to compounds into advanced machine learning or network-based models to predict accurate repositioning candidates. Example of papers: (Azad et al, Briefings in Bioinformatics, 2020), (Azad et al, Patterns, 2021, http://dx.doi.org/10.1016/j.patter.2021.100325)

Experience: Students need to have experience in programming and interest in data science

 

 

Project Title: Evolution of the Flagellar Motor Across Interfaces

Supervisor(s): Dr Matthew Baker

Description: In this project we explore the directed evolution of the flagellar motor in the lab by evolving it to swim under different energy sources and selecting for motility. We aim to explore how motility evolves across interfaces, when a bacterium faces a change in environment between, for example, H+ and Na+ environments, and how the bacteria adapts to dwindling nutrient across this interface. This project has scope for designing and building custom tanks to optimise bacterial evolution using 3D printing and prototyping, as well as investigating microbiology and bacterial motility in multiple dimensions using layered swim devices.

Experience: Some background in microbiology would be good!

 

 

Project Title: Contribution of Toll-like receptors in Helicobacter pylori-mediated gastric carcinogenesis 

Supervisor(s): Dr Natalia Castano-Rodriguez

Description: Upon recognition by pattern recognition receptors, in particular Toll-like receptors (TLRs), the bacterium Helicobacter pylori is known to disrupt the normal body homeostasis and induce acute inflammation, which may progress over time to stomach cancer. An increasing amount of evidence suggests that polymorphisms in the TLR genes, including TLR4 rs11536889, are involved in modulating these immune signalling pathways, thereby increasing the risk of gastric carcinogenesis. We will now investigate the underlying mechanisms using ex-vivo and in-vitro methods.

Experience: Knowledge and/or interest in immunology, genetics and microbiology

    

Project Title: Regulatory element prediction using deep learning 

Supervisor(s): A/Prof Emily Wong

Description: Enhancers are cis-regulatory elements that direct spatiotemporal expression. We are interested in using the latest methods to explore how to best decode these regulatory sequences. You will be exposed to both experimental and computational approaches used in high throughput analyses.

Experience: Machine learning/ statistics /  genetics genomics / molecular biology

    

 

 

 

 

 

School of Physics Projects

Project Title: Illuminating the Dark Universe with Gravitational Lensing

Supervisor(s): A/Prof Kim-Vy Tran

Description: This Project uses Gravitational Lensing to directly detect total matter halos at cosmological distances.  By measuring the properties of massive halos and comparing to predictions of how baryons couple to dark matter, we illuminate dark matter and constrain fundamental cosmological parameters.  The student will use multi-wavelength data-sets including imaging from the Hubble Space Telescope to help develop lensing models and determine galaxy scaling relationships.

Experience: None

 

Project Title: Sighting the Spotted Surfaces of Sun-like Stars

Supervisor(s): Dr Benjamin Montet

Description: Observations of the growth, decay, and locations of sunspots provide clues about the nature of the Sun's magnetic field. To understand the potential habitability of exoplanet systems, we want to understand the ways in which their host stars' magnetic fields are different from our own Sun's. One way to accomplish this is through observations of starspots. In this project, we will use exoplanet transits, the moments when a planet crosses the surface of its host star, casting a shadow along one stellar chord, to infer the distributions of starspots on stars of different ages, masses, and compositions. These data will enable us to characterise how these parameters affect stellar magnetic fields. This project will involve programming in Python; prior programming experience in any language will be helpful but not required.

Experience: None

 

Project Title: Theoretical Physics: atomic, nuclear, elementary particle, molecular, solid state, statistical physics, general relativity and astrophysics

Supervisor(s): Prof Victor Flambaum

Description: A choice of projects is available: •  Search for non-gravitational effects of Dark matter and Dark energy in atomic and astrophysical phenomena. •  Test of Unification theories and search for “new physics” using effects of violation of fundamental symmetries: Parity, Time reversal, Lorentz invariance and Einstein equivalence principle. •  Search for space-time variation of the fundamental constants of Nature. •  High precision atomic calculations which are needed for the topics mentioned above and for search for superheavy nuclei, including  nuclear island of stability and “strange matter”, using astrophysical and laboratory atomic spectra.

Experience: Third year student who has good knowledge of quantum mechanics

 

Project Title: Atomically thin van-der Waals materials

Supervisor(s): Dr Feixiang Xiang & Prof Alex Hamilton

Description: Graphene, a single layer of carbon atoms with honeycomb lattice structure, shows many exotic physics and promising properties for device applications. Stacking different layers together provides a degree of freedom to change electronic properties of graphene, such as electronic band structures. In this summer project, the successful applicant will work with a team from QED group from School of Phyiscs at UNSW to explore effect of different stacking order on electronic properties of ABA- and ABC- stacked trilayer graphene. The successful applicant will participate in fabrication of van der Waals heterostructure and measuring their electronic properties in an environment of ultracold temperatures and high magnetic fields.

Experience: None

 

Project Title: Hole spins in strained Germanium 

Supervisor(s): Dr Matthew Rendell & Prof Alex Hamilton

Description: Despite being used in the first transistor, Germanium was replaced by Silicon in most semiconductor devices. Recently, strained Germanium has had a resurgence in nanoscale electronics due to its interesting quantum properties including spin-orbit interactions and coupling to superconductors. These properties make strained Germanium useful for quantum computing using spin qubits, low energy topological electronics, and exotic superconducting states. In this project you will have hands on lab experience measuring the quantum properties of holes in strained Germanium electronic devices using cryogenic systems and low noise measurement techniques.

Experience: None

 

Project Title: Hole spins for quantum information

Supervisor(s): Dr Scott Liles & Prof Alex Hamilton

Description: The understanding of the quantum mechanical properties of positively charged holes in nanoscale electronic devices is far from complete, despite the fact that your mobile phone contains billions of transistors that use holes. This is because although undergraduates are often taught that valence band holes are essentially just heavy electrons, with a positive charge and a positive effective mass, holes are spin-3/2 particles whereas electrons are spin-1/2. The spin-3/2 nature of holes means they make excellent spin quantum bits, and this project will involve hands on laboratory research to develop new research tools to study how to read and manipulate hole spin qubits. See http://www.phys.unsw.edu.au/QED for more details.

Experience: None

 

Project Title: Measuring the quantum capacitance

Supervisor(s): Dr Joe Hillier & Prof Alex Hamilton

Description: Classical capacitance is a purely geometrical effect. The quantum capacitance results from the discrete energy levels of electrons in solids, and provides unique insights into the electronic properties of quantum devices (even if they are insulating). This project will design and test new experimental setups for measuring the quantum capacitance at ultra-low temperatures in advanced semiconductor devices.

Experience: None

 

Project Title: Developing an undergraduate experiment to measure the Quantum Hall Effect

Supervisor(s): Dr Daisy Wang & Prof Alex Hamilton

Description: In the classical Hall effect, a current flowing through a conductor placed gives rises to a transverse Hall voltage that depends linearly on the applied magnetic field. The 1985 Nobel Prize in Physics was awarded for the totally unexpected discovery that the Hall resistance in thin two-dimensional conducting sheets in silicon transistors is not linear, but jumps in steps, and depends only on the electron charge and Planck's constant. In 1988 another Nobel Prize was awarded for the unexpected discovery of the fractional quantum Hall effect, which seemed to indicate electrons were breaking up into fractionally charged objects. This project will measure quantum Hall effect in two-dimensional transistors at temperatures below 2 Kelvin and magnetic fields up to 9 Tesla, and develop an instruction guide for higher year physics lab students.

Experience: None

 

Project Title: Quantum computing algorithms on the silicon architecture

Supervisor(s): Dr Casey Meyers & Prof Michelle Simmons

Description: Quantum algorithms have been shown to provide a way of speeding up certain computationally demanding classical algorithms, across an extensive range of problem types, including optimisation, solving linear algebra problems and factoring large numbers. In these projects we will examine two broad aspects of quantum algorithms: how best can quantum algorithms be formulated to run on near-term physical devices, specifically the silicon hardware being developed at CQC2T; what other potential real-world applications can quantum algorithms solve.

Experience: None

 

Project Title: Hall bar measurements in phosphorus-doped silicon

Supervisor(s): Dr Sam Gorman & Prof Michelle Simmons

Description: The Hall voltage is an induced voltage observed when a flow of electrons is placed in a strong magnetic field. The measurement of the Hall voltage can be used to elucidate many properties about a material and observe fundamental phenomenon in low-dimensional systems. In this project the student will measure Hall bar fabricated from phosphorus-doped silicon in a dilution refrigerator to characterise the device.

Experience: None

 

 

 

 

 

 

School of Mathematics & Statistics Projects

Project Title: Data augmentation with MCMC for addressing class imbalance via Bayesian neural networks

Supervisor(s): Dr Rohitash Chandra

Description: Although novel deep learning methods have been developed, class imbalance problems pose a major challenge for machine learning. A major strategy to address these problems has been through data augmentation methods such as over-sampling methods (SMOTE) and generative adversarial networks (GANs). However, these methods do not cater for uncertainty quantification in model predictions given large uncertainty in the data generation process. In this project, we present a Bayesian framework that utilises Markov Chain Monte Carlo (MCMC) sampling for data augmentation in class imbalance problems. The framework addresses the limitation of uncertainty quantification in model predictions via Bayesian neural networks. 

Experience: Knowledge of machine learning/computational statistics and exceptional programming skills (Python)

Project Title: Extreme value forecasting via GAN Data Augmentation 

Supervisor(s): Dr Rohitash Chandra

Description: Data augmentation with generative adversarial networks (GANs) has been popular for class imbalance problems mainly for pattern classification and computer vision related applications. Extreme value forecasting is a challenging field which has various applications from finance to climate change related problems. In this project, we present a data augmentation framework via GANs for extreme value forecasting. In this framework, an ensemble is utilised to distinguish forecasting for extreme values in order to use data augmentation appropriately. We use deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and provide multi-step ahead prediction.

Experience: Knowledge of machine learning/computational statistics and exceptional programming skills (Python)

 

Project Title: Social network analysis and graph theory for sustainability science

Supervisor(s): Dr Sahani Pathiraja

Description: This project will involve investigating methods in network science and graph theory to obtain improved representation of social networks. The end goal is to better represent and understand how behavioural change can occur on a large scale to help promote transitioning to a society that utilises resources more sustainably. The focus of the summer research project will be on implementing and analysing network models and their mathematical properties.

Experience: Enrolled in UG Mathematics or Statistics program required; Machine learning and coding skills desirable

 

Project Title: Distilling the ocean’s role in climate using phase diagrams.

Supervisor(s): Associate Professor Jan Zika

Description: Understanding how much and to what depth heat will be pumped into the ocean is critical to predict future surface temperature and sea-level rise. This student will investigate vertical heat transport in the ocean using novel thermodynamic diagrams. Using such diagrams, which have origins in classical thermodynamics, one can relate the circulation to surface heating and cooling processes and mixing. These diagrams also lend themselves to the application of novel image processing/unsupervised machine learning techniques. The student will make use of the most recent observations and combine these novel machine learning and image processing techniques to understand the drivers of recent ocean change.

Experience: Undergraduate major in Mathematics and/or Physics

 

 

School of Psychology Projects

Project Title: Blood Donation – Emotions Past, Present, and Future

Supervisor(s): A/Prof Lisa Williams

Description: A growing body of research points to the importance of emotional experiences in determining if people decide to donate blood. This project is part of an active line of research at the nexus of social, health, and applied psychology on how both positive and negative emotions drive decisions to donate blood, with a focus on emotions recalled from the past, felt in the moment, or anticipated from the future. Recent studies led by students in the lab have identified how gratitude and other positive emotions boost and anxiety detracts from people’s desire to donate blood – and have developed methods to shape those emotions so as to improve willingness to donate. This project will advance such lines of research.

Experience: None

 

Project Title: Understanding the Psychological Impact of Missing Family

Supervisor(s): Dr Belinda Liddell, Dr Yulisha Byrow & Prof Angela Nickerson

Description: This project would involve working in the Refugee Trauma and Recovery Program in the School of Psychology at UNSW. We are currently undertaking a longitudinal study following refugee participants who have family who are missing or have disappeared as a result of forced displacement. The goal of this study is to determine the effects of this experience on mental health, and the psychological and social processes involved. This Vacation Research Scholarship would involve providing support to this project by (1) monitoring participant progress, (2) contributing to recruitment and participant engagement efforts, (3) providing research support in terms of data management and entry, and (4) contributing to lab activities including lab meetings. This project would suit a student with an interest in refugee and human rights issues, who is interested in learning about how scientific research can be undertaken with vulnerable populations, and how research findings can contribute to changes in policy and practice. This Scholarship would lead to increased knowledge regarding the experiences of refugees in Australia, refugee mental health, hands on experience in culturally sensitive research methods with refugees, and greater understanding of working with industry stakeholders (e.g., NGOs) to identify research questions and implement findings.

Experience: None

 

Project Title: Measuring individual differences in face perception bias 

Supervisor(s): Dr David White

Description: The student will join a project team investigating novel techniques for measuring ‘bias’ in humans and facial recognition technology. In this part of the project, the student will help develop new methods for measuring bias in decisions that people make with faces: for example deciding who someone is, what age they are, how trustworthy they are. Some people are more likely to be biased in these decisions, favouring one response (e.g. 'same person') over the other (e.g. 'different person'). What causes these biases and can we design tests to measure them?

Biased decisions can have important impacts on decisions made in applied settings. For example, the 'other-race bias' can cause disproportionate number of errors in eyewitness identification decisions made towards ethnic minorities, leading to racial disparities in the rate of wrongfully criminal convictions. The summer project will aim to develop tests that can begin to measure the likelihood that individuals will make 'biased' face processing decisions. 

Experience: None required.Some experience with computer programming / data analytics helpful. 

 

Project Title: The role of attention in colour constancy

Supervisor(s): Dr Erin Goddard

Description: How do our brains separate raw colour information into different sources? The apparent surface colour of objects depends on how we interpret the lighting conditions of the scene (e.g. #theDress), a process known as ‘colour constancy’. To better understand the neural processes involved in these phenomena, this project will test the role of attention in the perceptual separation of different colour sources. The project will involve collecting measurements of perceived colour for human participants using a computer and specialised display. No prior experience is necessary: data collection and analysis will involve working with Matlab and some statistical analyses, but all required skills will be taught as part of the project. In the lab, we also investigate human visual perception using fMRI and magnetoencephalography (MEG), and summer students will have the option of also being involved in these experiments if they are interested.

Experience: None

 

 

Project Title: Applying time series analysis / machine learning methods to Magnetoencephalography (MEG) data

Supervisor(s): Dr Erin Goddard, A/Prof Gustavo Batista

Description: Magnetoencephalography (MEG) is a non-invasive human brain recording method where neural responses can be measured while the participant views a stimulus and/or performs a task. This creates rich datasets (e.g. 160 sensors / channels from across the brain, recorded at 1000 Hz). To help gain insights into brain function, machine learning approaches can be useful for measuring the stimulus-related and/or task-related information in the neural signals. This project will involve extending on existing methods by trying out approaches from time series analysis on existing MEG datasets. This project is part of a collaboration between Dr Erin Goddard (Psychology), who uses MEG to address questions of visual neuroscience, and A/Prof Gustavo Batista (Computer Science), who is an expert in machine learning and time series analysis. In this project, the student will work with both Dr Goddard and A/Prof Batista to plan a new approach, write code to execute the analysis, and try it out by applying the approach to existing MEG datasets. This would suit a student with interest and experience in both psychology and computer science and/or machine learning.

Experience: Prior experience in coding in at least one of Matlab or python is required. If you are unsure whether you have enough experience, you are welcome to contact Dr Goddard at erin.goddard@unsw.edu.au

 

Project Title: A role for epigenetics in the development of nicotine dependence

Supervisor(s): Dr Kelly Clemens

Description: Nicotine is one of the most widely abused drugs as it is extremely difficult to give up. This is strange considering that it is not intensely rewarding and doesn’t produce a ‘high’ like many other drugs of abuse. One reason for this might be that nicotine appears to lead to a range of epigenetic changes that alter how the brain encodes information in the immediate environment – such information that can trigger intense cravings and relapse (e.g. others smoking). Epigenetic changes can mean this information might be encoded more robustly, therefore conferring it with greater power to trigger cravings in the future. This project will assess whether drugs that produce epigenetic changes in the brain increase the likelihood of developing nicotine dependence. It will involve animal studies and potentially some wet lab work. Although an understanding of neuroscience or molecular biology would help, it is not a prerequisite.  

Experience: None

 

Project Title: How are secondary fear memories encoded and consolidated in the brain?

Supervisor(s): Dr Nathan Holmes & Dr Jessica Leake

Description: Studies investigating the biological basis of fear memories typically use protocols involving a single dangerous event. In reality, traumatic experiences, such as abuse, are often repeated and this has implications for how fear memories are processed in the brain. Our laboratory has found that forming an initial fear memory depends on a cascade of biochemical processes in a specific region of the brain, the amygdala. This includes: 1) the activation of NMDA receptors, whose biophysical properties make them ideally suited to detecting coincidences between environmental stimuli and danger; and 2) synthesis of new proteins, which is thought to be critical for stabilization (or consolidation) of new fear memories so that they can be activated by appropriate retrieval cues. However, we have also shown that the same mechanisms are not required for forming a second, related fear memory. The goal of this research is to determine how secondary fear memories are encoded and stored in the brain. This goal will be achieved using a combination of behavioural and pharmacological manipulations in laboratory rats.

Experience: None

    

 

Project Title: Brain dynamics for adapting to avoidable versus unavoidable danger 

Supervisor(s): Dr Philip Jean-Richard-dit-Bressel 

Description: The adaptive response to negative events depends on whether those negative events are avoidable or unavoidable (response-dependent vs. -independent, respectively). Fittingly, we are equipped with separate learning systems to adapt behaviour according to these different scenarios. These are thought to depend on partially overlapping brain networks, with the basolateral amygdala (BLA) region acting as a shared hub for response-dependent and -independent aversive learning. However, it remains poorly understood how different aspects of aversive learning and decision-making are handled by different BLA networks. This project seeks to measure and manipulate real-time activity in key BLA circuits using cutting-edge neuroscience techniques to understand how we identify and adjust to different aversive conditions.  

Experience: None required. Experience with rodent handling and wetlab work preferred. 

    

 

Project Title: Unhealthy diets and cognitive and brain health

Supervisor(s): Dr Tuki Attuquayefio

Description: There is clear evidence that unhealthy diets are harmful to one’s physical, cognitive and brain health. Diet may have an important role in the trajectory of cognitive and brain health across the lifespan, and specific brain areas may be particularly sensitive to what you put in your body. While various small-scale studies point also suggest this, the use of large-scale open-source data may prove invaluable. The UK Biobank is the world's largest biomedical database, containing in-depth health, cognitive and brain imaging data from half a million UK participants. The project will investigate the relationship between diet, cognition and brain health across the lifespan using this open-source database. The successful candidate will have a passion and curiosity for research, and willingness to learn new skills in statistics and research methodology.

Experience: None required. Experience with R software desirable.

 

Project Title: Decision-making in addiction

Supervisor(s): Prof Gavan McNally

Description: This project studies how decision making processes are altered in addiction. It uses a mouse model of choice to study the core psychological and brain mechanisms of choosing to pursue a reward despite adverse consequences. Students will be exposed to behavioural training, computational modelling, and in vivo two colour calcium imaging.

Experience: None

 

Project Title: Developmental analysis of memory

Supervisor(s): Prof Rick Richardson 

Description: In this project we will explore a topic related to developmental differences in learning and memory. For example, across many species infants forget more rapidly than adults, a phenomenon  referred to as infantile amnesia. In addition, adolescents (again, across many species) exhibit different patterns of emotional regulation than do animals that are younger or older. For example, adolescents are impaired at learning to inhibit fear (i.e., they exhibit an impairment in fear extinction). In this project we will explore such an issue at the behavioural, and maybe at the neural level (depending, in part, and the student’s interest). 

Experience: The project will involve working with rats, and while some experience handling rats would be good, it is not required as training will be provided

School of Biological, Earth & Environmental Sciences

Project Title: Using data from videogames to explore theoretical concepts in ecology and evolution

Supervisor(s): A/Prof Michael Kasumovic

Description: Videogames are a unique system in which to ask theoretical questions in ecology and evolution because of the 'honest' data that they are able to collect. In this data-driven project, a student will be using data collected from videogames to explore different ecological and evolutionary theories. There are three possible projects that involve exploring: (1) The evolution of signalling of prey and learning in predators, (2) The evolution of communication and signal transmission between mates, and (3) Exploring the behaviour of players to examine life-history trade-offs and foraging decisions. The student will be able to choose which project they would like to explore.

Experience: A strong foundation using R - this project is fully data based and will require students to be able to program in the statistical language of R

 

Project Title: Looking at coral cells to understand disease: histopathology of coral samples from Norfolk Island, South Pacific

Supervisor(s): A/Prof Tracy Ainsworth & Charlotte Page

Description: Increasingly organisms are being exposed to environmental stressors that are conducive to disease outbreaks, including changes in climate and pollution from human activities on land. When disease impacts foundational species knock-on effects can be widespread triggering shifts in dominant habitat cover causing degradation. On Norfolk Island, South Pacific we recorded disease as impacting two foundational hard coral taxa (Montipora and Acropora). This project will focus on analysing coral tissue samples from diseased and healthy colonies using an approach called histopathology. Histopathology is a commonly used technique across many fields that refers to the microscopic examination of tissue structure in order to study the manifestation of disease. The student will gain skills in microscopy, coral anatomy, coral health and quantitative histological analysis. This project will be lab and computer based.

Experience: NA

 

Project Title: Palaeobotany of a Miocene Lagerstätte

Supervisor(s): Dr Matthew McCurry

Description: Excavations have recently taken place at McGrath’s Flat, uncovering fossilised leaves belonging to angiosperms (~11 -16 myo). This project will aim to classify morphotypes of the angisperm leaves found within the deposit and then to test whether sampling depth correlates to differing leaf morphologies. The project will provide important information on the tempo and timing of aridification at the fossil site.

 

Experience: Experience working with museum specimens, an interest in palaeobotany. 

 

 

Project Title: Developing a peat combustion risk index for Coastal Upland Swamps of the Sydney Basin

Supervisor(s): Dr Tanya Mason

Description: Ground fires occur when organic soils are consumed by smouldering combustion. Such fires, while less dramatic than aboveground fuel combustion, are difficult to extinguish, consume physical substrates and kill plant roots and propagules. Upland swamps are vulnerable to ground fires where underground mining disrupts groundwater availability or where climate change initiates increased evapotranspiration relative to precipitation. The project seeks to understand the risk of ground fire under different soil moisture scenarios. Peat samples from four upland swamp communities have been collected. Soil moisture of peat samples will be manipulated to simulate different hydrological regimes associated with mining or climate change in upland swamp communities. A controlled laboratory peat combustion trial will be established. Metrics such as soil wet and dry weight, bulk density, gravimetric soil moisture and percent consumption of samples will be recorded. The aim is to develop peat fire risk indices and upland swamp fire management guidelines.

Experience: NA

 

Project Title: Mapping hail boundary conditions in Australia

Supervisor(s): Dr Tim Raupach (CCRC)

Description: Hail causes major damages in Australia, but predicting its occurrence and how it may be affected by climate change present challenges. For severe thunderstorms that form hail to occur, certain “ingredients” must be present: the atmosphere must be unstable and so prone to forming updrafts, there must be moisture available, and winds that differ by height can make the thunderstorm more severe. Further, the temperature of the atmosphere affects how much hail melts as it falls. In this project, the student will use high-resolution data to examine the state of the atmosphere in Australia over the last four decades, and produce maps showing hail boundary conditions – that is, where and when hail could or could not occur.

Experience: Some python programming experience required.

 

 

Project Title: Albedo effects on Saharan convection

Supervisor(s): Dr Tim Raupach (CCRC)

Description: Mid-level clouds form with a predictable daily cycle over the Sahara in north Africa. The clouds affect the radiation budget for the region, but form in thin layers that are challenging to correctly simulate in weather and climate models. It has been observed that these clouds tend to form over regions with lower surface albedo and high surface elevation – but the explanation of the processes at play remains uncertain. In this project the student will run idealised model simulations to experiment with different surface properties, varying albedo and elevation, to try to explain these observations.

Experience: Experience running weather models a plus but not required.

 

 

Project Title: Sex-biased dispersal in little penguins

Supervisor(s): Scientia A/Prof Lee Ann Rollins

Description: Males and females of a species often differ in their dispersal tendencies. Knowledge of these patterns can be useful in conservation management. This project focuses on using molecular techniques to determine the sex of little penguins (Eudyptula minor) and then using this information in conjunction with existing molecular data to study how dispersal patterns differ between the sexes. The outcomes of this project will be used to form management advice for this species.

Experience: Applicants with a conceptual understanding of PCR and experience using a pipette are desired.

    

 

School of Materials Science and Engineering

Project Title: Metal Oxides for Energy Storage Devices

Supervisor(s): Dr Pramod Koshy & Dr Sajjad Mofarah

Description: Fabrication and assembly of metal oxides for use in primary coin cell batteries 

Experience: Basics of Materials Synthesis, Chemistry and/or Electrochemistry

 

Project Title: Carbon based Materials for Rapid Charge Storage Devices 

Supervisor(s): Dr Pramod Koshy & Dr Sajjad Mofarah

Description: Fabrication of supercapacitors with high power density using carbon-based materials (graphite) synthesised 

Experience: Basics of Materials Synthesis, Chemistry and/or Electrochemistry

 

Project Title: Design and fabrication of Oxide Nanostructures for Water Splitting

Supervisor(s): Dr Pramod Koshy & Dr Sajjad Mofarah

Description: Controlled fabrication of nanopowders with optimized morphologies for the electrocatalytic generation of O2

Experience: Basics of Materials Synthesis, Chemistry and/or Electrochemistry

 

 

Project Title: Layered Materials for Li-ion Rechargeable Batteries

Supervisor(s): Dr Pramod Koshy & Dr Sajjad Mofarah

Description: Design and development of layered transition metal oxides of Ni and Mn for rechargeable Li-ion batteries

Experience: Basics of Materials Synthesis, Chemistry and/or Electrochemistry

 

Project Title: High Entropy Alloys for Hydrogen Storage 

Supervisor(s): Dr Pramod Koshy & Dr Sajjad Mofarah

Description: Characterisation, optimising fabrication treatments of high entropy alloys for storage of hydrogen in the alloys

Experience: Basic Chemistry and Materials Science 

 

 

Project Title: Carbonaceous Nanomaterials for On-board Hydrogen Storage System 

Supervisor(s): Dr Pramod Koshy & Dr Sajjad Mofarah

Description: Design and development of light-weight carbonaceous nanomaterials such as carbon nanotubes and carbon nitride etc. for storing hydrogen in fuel cell vehicles. 

Experience: Basic Chemistry and Materials Science 

 

 

Project Title: Carbon Nanotube/Graphene Composite Thin Films for Effective Thermal Management For Electronic Devices 

Supervisor(s): Dr Pramod Koshy & Dr Sajjad Mofarah

Description: Synthesis of carbon nanotube/graphene hybrid film and develop scalable printing method to achieve large-scale, variable-size thin films with excellent thermal conductivities and mechanical strength.  

Experience: Basic Chemistry, Materials Science and Physics 

    

Project Title: Advanced Single Crystal Piezoelectric Materials for Naval SONAR Applications 

Supervisor(s): A/Prof John Daniels

Description: At present, the active elements of naval sonar systems in use are almost exclusively built from polycrystalline ceramic PZT materials due to their cost advantage and long-term reliability.  Single crystal piezoelectric materials have enhanced performance capabilities in comparison to polycrystalline ceramics but are still restricted in their use due to limited long-term operational knowledge and their expense to produce.  This project will contribute to the characterisation of novel single crystal piezoelectric material compositions for their application in naval sonar systems.  Working on this project you will learn a variety of piezoelectric materials characterisation methods, including electrical and mechanical property measurements as well as structural analysis using x-ray diffraction.  The project is in collaboration with the Defence Science and Technology Group.  Due to security restrictions of the project partners, the project is only open to students with Australian Citizenship. 

Experience: None