Project Title: Effects of opioids on brain barriers

Supervisor(s): Associate Professor Kelly Clemens, Dr Sarah-Jane Leigh

Description: Long-term opioid use results in brain injury and cognitive deficits. While some of these issues likely arise because opioids directly affect the brain, recent studies also suggest that prolonged opioid use can harm the barriers that protect the brain, such as the blood-brain barrier and the blood-cerebrospinal fluid barrier. These barriers are crucial for maintaining normal brain function and behaviour, and they could contribute to the lasting effects of opioid use. In this research project, we will investigate how methadone treatment affects the structure of these brain barriers in rats. We will study rats that have been treated with methadone, comparing those with and without a treatment aimed at improving the integrity of these barriers. This study will involve rat handling as well as using techniques like immunofluorescence to analyze the barriers in the brain.

Experience: None

Project Title: The role of attitudes towards disability among older adults in forming preferences about future health, care and medical treatments

Supervisor(s): Dr Craig Sinclair

Description: Previous research has shown that values, preferences and decisions about potential future treatments or care decisions in the context of life limiting illness are influenced by factors including education, health literacy, experience as a carer for others, anxiety and cultural background. As decisions about treatment in the context of chronic and/or life limiting illness often intersect with judgements about quality of life and/or disability, it is possible that societal and individual perceptions and attitudes towards disability may impact on this decision-making. An existing dataset from an online experiment conducted during 2024 has assessed implicit attitudes towards disability using the Implicit Association Test. This project will involve analysis of this existing data, to determine whether the implicit attitudes towards disability among older adults are predictive of their responses to a series of hypothetical scenarios, manipulated to be provide more or less positive interpretations of living with disability.

Experience: Some familiarity with statistical analysis and data visualisation in R would be required. The project involves analysis and writeup of an existing dataset, direct contact with participants would not be a requirement.

Project Title: Applying machine learning to understand attention in the brain

Supervisor(s): Dr Kelly Garner, Professor Mike Le Pelley

Description: We're combining non-invasive brain imaging (EEG) with machine learning, to predict attention lapses in humans. The project involves helping out with collecting and analysis EEG data, including the application of machine learning to the data.

Experience: This project would suit someone with a background in psychology who is keen to learn more about machine learning, or someone with a background in computer science or engineering who is keen to learn more about the human brain.

Project Title: Development of memory

Supervisor(s): Professor Rick Richardson

Description: Fear memory in developing rats

Experience: None.

Project Title: Mechanisms of punishment learning

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

Description: When our actions have negative consequences, we are usually able to learn about this relationship to avoid making undesirable actions again in the future (“punishment learning”). Our lab seeks to understand how this learning happens, when and why it fails, and how decisions regarding punishment are resolved, psychologically and biologically. We investigate these processes using state-of-the-art neuroscience techniques in rodent models and specially-designed experimental tasks in humans. A summer internship with us will likely involve assisting a PhD candidate with their research, which will involve direct training and hands-on experience with various parts of our research process.

Experience: None

Project Title: Drink more to eat less? Impact of hydration status on palatable food intake

Supervisor(s): Dr Zhi Yi Ong

Description: Water and food intake are interdependent such that food intake is accompanied by prandial drinking while water deprivation reduces food intake. In obese individuals, this relationship may be altered as they drink less water and are less sensitive to thirst. The underlying mechanisms are however unclear. This project will therefore examine how feeding behaviours are altered under different hydration conditions in an animal model of diet-induced obesity. The student will assess feeding behaviours across a suite of behavioural tasks, perform physiological measurements and quantify brain neural activation.

Experience: Experience with rat handling is desirable though not necessary

Project Title: Restoring gut-brain signal sensitivity to overcome overeating

Supervisor(s): Dr Zhi Yi Ong

Description: Overeating is a major contributor to the increasing rates of obesity worldwide. Individuals with reduced gut-brain signal sensitivity are more likely to overeat and gain weight. Here, we will examine whether restoring gut-brain signal sensitivity can prevent overeating. This project will involve genetic manipulation strategies to restore gut-brain signal sensitivity, home-cage food intake monitoring and behavioural tasks to measure feeding behaviours, and brain histology.

Experience: Experience with rat handling is desirable though not necessary

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, Associate Professor 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: Machine learning for climate

Supervisor(s): Professor Steven Sherwood, Dr Abhnil Prasad, Dr David Fuchs

Description: Most of our understanding of changes in atmospheric temperature and wind come from reanalysis products, but these are problematic for looking at small changes over long time periods. The student will examine a new homogenised global radiosonde dataset for climate-change signals including trends in upper tropospheric temperatures and winds.

Experience: Familiarity with Python will be required for this project

Project Title: Analysis of radiosonde data

Supervisor(s): Professor Steven Sherwood

Description: Australia’s most hail-prone regions are on the east coast from north of Brisbane to south of Sydney. However, the largest hailstone ever recorded in Australia fell in the sub-tropics, just north of Mackay, and the possibility of hail occurrence extends well into the tropics. In particular, a region around Burketown in Queensland shows as a hotspot of hail probability in radar, satellite, and hail-proxy records. In this project, we will investigate hail occurrence in convection-resolving simulations of the atmosphere around Burketown. The student will gain experience in analysing the output from high-resolution weather models, in atmospheric science, and in scientific programming. The project will increase our understanding of the atmospheric conditions leading to hail formation in the (sub-)tropics, a region in which hail occurrence is not well understood.

Experience: None

Project Title: Variations in water mass formation and heat uptake

Supervisor(s): Dr Zhi Li, Associate Professor Alex Sen Gupta

Description: Oceans absorb over 90% of the excess human-generated heat accumulated in the Earth’s climate system, moderating atmospheric temperature rises. Warming of the ocean has been pervasive worldwide, spreading from the surface to the deep sea and from the tropics to the poles, but its distribution is not uniform. The observed ocean warming hotspots align with the locations of certain water masses in the subtropical Pacific and Atlantic Oceans and in the Southern Ocean. Previous studies show that those mode and intermediate waters, including subtropical mode waters in the Atlantic and Pacific Oceans, Subantarctic Mode Water, Antarctic Intermediate Water, and North Pacific Intermediate Water, are key in absorbing and transferring excess heat from the surface to the ocean’s interior. Yet little is known about the mechanisms that control the increased heat uptake by those water masses. This project will be mainly based on observations and reanalysis datasets, aiming to provide insights into the dynamics of heat uptake and redistribution within the ocean. In this project, you will work on understanding the variations of water-mass formation and heat uptake, linking changes in water-mass formation to variations in wintertime mixed-layer depth, subduction and ocean mixing, and variations in the oceanic transport of heat by Western Boundary Currents.

Experience: A student applying for this project should be proficient in the use of MATLAB or Python and have a background in oceanography.

Project Title: Processing urban morphology for computer vision applications in city’s weather and climate

Supervisor(s): Dr Negin Nazarian, Dr Jiachen Lu, Dr Sanaa Hobeichi

Description: Complex flow patterns within urban environments are significantly influenced by the diversity of urban layouts but have only been studied from generalizations based on conventional urban geometrical parameters in climate models. However, the inner- and ultra-variability of cities’ layouts including the street orientations, building shapes, and building height distributions challenge the generalization validity. Considering the scope of the study is for global cities, the validation work is better assisted by computer vision techniques that require a strong database of urban morphology. Based on the recent progress in satellite data processing (e.g., OpenStreetMap (OSM) and Microsoft Building Footprints) and building height estimation (World Settlement Footprint (WSF)), the high-resolution urban morphology is ready for this purpose. In this project, the selected student will learn and apply image pre-processing techniques for computer vision applications in weather and climate. The data produced will contribute to enhancing the understanding of urban heterogeneities' impact on climate models. In this project, the student will be coding and adapting existing scripts.

Experience: The applicant needs to have programming experience in Python to be successful.

Project Title: Validating and preparing an Australian drought inventory for machine learning training

Supervisor(s): Dr Sanaa Hobeichi, Dr Elisabeth Vogel

Description: This project aims to validate a comprehensive drought inventory and prepare it for Machine Learning training. The inventory is compiled from over a hundred drought reports and climate statements and includes detailed information on the locations, times, and impacts of past droughts on Australian communities and ecosystems. Examples of documented impacts include statements such as "crops are cut for hay and silage”, "water supplies in major population centres have been affected”, and "inadequate water availability in the main storage dam”. The selected student will use various climate observations, such as streamflow and precipitation data from monitoring stations, crop yield datasets, and satellite-derived vegetation indices to validate the reported drought impacts. This validation process is essential to identify any erroneous information and ensure the accuracy of the drought database. The validated database will be a valuable resource for advancing drought research and developing accurate drought models using Machine Learning.

Experience: Students need to have experience in Python or R programming to be considered for this project.

Project Title: Do regional climate models rain too much? 

Supervisor(s): Dr Phuong Loan Nguyen, Professor Lisa Alexander

Description: Regional climate models (RCMs) derived by dynamically downscaling global climate models (GCMs) over restricted domains (e.g., cities, states, countries), provide stakeholders with the high-resolution data needed for climate impact assessments and decision-making at regional scales. However, previous studies indicate that precipitation in RCMs is more intense than their forcing GCMs over many parts of the world including Southeast Asia, Europe, and East Asia. In this project, we will compare rainfall intensity patterns from RCMs and GCMs from a currently under-studied global sub-region (e.g., Africa) using multiple statistics- and process-based metrics within a common benchmarking framework. The student will gain experience in analysing large amounts of climate information from observations through to regional and global climate model output. The project will help to address common modelling limitations with the aim of ultimately improving rainfall projections in RCMs.

Experience: To complete this project, experience with Python is essential.

Project Title: Precipitation uncertainty from satellite products: a hemispheric intercomparison

Supervisor(s): Joaquin Blanco, Professor Lisa Alexander

Description: Uncertainties in rainfall estimates from a range of satellite retrievals challenge our understanding of small and large-scale processes associated with Earth’s rainfall (annual mean, variability, extremes) as well as our efforts to validate precipitation simulated by Global Climate Models. This project will compare and analyse similarities and differences in observed precipitation uncertainties between Southern and Northern hemispheres, focusing on mid-to-high latitudes over oceanic regions (i.e.: the Southern Ocean vs North Atlantic/ Pacific).

Experience: The project requires essential programming skills in Python / JupyterLab to analyse data from the precipitation database FROGS hosted on the Australian supercomputer Gadi.

Project Title: The 2023/2024 South West Indian Ocean Marine Heatwave

Supervisor(s): Dr Neil Malan

Description: During 2023 and 2024 a 7-month long marine heatwave occurred in the southwest Indian Ocean, centred on the islands of Mauritius, Reunion and Madagascar. Marine heatwaves are discrete, prolonged, anomalously warm water events, known to have significant impacts on marine ecosystems. However, initial observations and eyewitness reports of the 2023/2024 SW Indian Ocean event indicate that not all areas experienced coral mortality as high as could be expected, perhaps due to the marine heatwave being confined to only the very surface layers. Within this project, you will utilize satellite and ocean profile data to explore the spatial and depth extent of the 2023/2024 SW Indian Ocean marine heatwave and learn skills in oceanographic data and extreme event analysis.

Experience: Experience in data analysis in Python, as well as some background in marine or environmental science is required.

Project Title: How does mother's environment influence offspring development?

Supervisor(s): Prof. Russell Bonduriansky

Description: This project will investigate the effects of ambient environment on offspring development and phenotype in a clonally reproducing springtail (Folsomia candida). Clonal reproduction makes it possible to create isogenic lineages and investigate effects of key environmental factors while controlling for genetic variation. An experimental manipulation of maternal ambient environment will be carried out and offspring traits will be quantified. Participating in this project will help you develop your skills in experiment design, lab techniques, statistical analysis, and scientific writing.

Experience: First and second year biology courses

Project Title: Nest interactions of an important seabird in the South Pacific

Supervisor(s): Prof. Richard Kingsford and Simon Gorta

Description: Seabirds are one of the most threatened, yet functionally important vertebrate groups in marine and island ecosystems. They are subject to a range of threats on land and at-sea, and identifying, monitoring, and managing these threats when possible is important for island and marine ecosystem management. In this project, you will work with UNSW researchers and environmental managers from Norfolk Island to analyse photographs and other data collected from nesting Sooty Terns. You will help to identify important intra- and interspecific interactions influencing seabird breeding success (e.g., egg predation), and the findings will be used to support conservation management.

Experience: Useful but not critical to have experience with data management, R, and camera traps.

Project Title: Chick adoption by incubating seabirds

Supervisor(s): Prof. Richard Kingsford and Simon Gorta

Description: Adoption occurs to varying degrees in wild animals and has been reported to varying extents in seabirds. This can offer benefits and/or costs to both the chick and its adoptive parents. In this project, you will synthesise available literature on adoption in wild animals (particularly seabirds) and process camera trap imagery of breeding Sooty Terns from Phillip Island (Norfolk Island, South Pacific) to report previously undescribed adoption behaviour in this species.

Experience: Useful to have experience with data management, scientific literature synthesis, and R.

Project Title: Uncovering dark lineages in the Tree of Life: documenting new plant bug species from Australia

Supervisor(s): Prof. Gerry Cassis

Description: It is estimated that there are ca. 10 million planetary species, yet only 30% are described. Australia is one of the least described biotas amongst OECD countries and is yet defined as one of 17 megadiverse countries. The taxonomic impediment (= undescribed biota) in Australia is greatest for insects, where is it estimated that more than 250,000 species are required to be described and are sometimes referred to as 'dark taxa'.

The Australian Academy of Sciences has released the Biosystematics plan to document all species in Australia within a generation (https://www.science.org.au/files/userfiles/support/reports-and-plans/201...). This plan is being enacted by the AAS Taxonomy Australia steering committee, of which Prof. Cassis is a member.

In line with these gaps in the Tree of Life and the taxonomic tree of life, Prof. Cassis is proposing a summer project for a student to undertake an integrated taxonomic study of a lineage of plant bugs belonging to the family Miridae. The UNSW Insect Lab has described ca. 400 new species over the past 15 years, yet most of the species are still to be described. This project will use combined molecular sequences and comparative morphological data to: 1) diagnose and describe of a clade of new species in the subfamily Orthotylinae; 2) construct a total evidence phylogeny of the former orthotyline clade; and, 3) place the new clade in the Miridae Tree of Life."

Experience: Training in biological sciences. Preferred if student has completed the BIOS3221 course.

Project Title: Observing the Behaviour and impact of Southern eagle ray Bioturbation on a shallow water seagrass ecosystem.

Supervisor(s): Professor Ian Suthers, Professor Tracy Ainsworth, Professor Tracy Rogers, Professor Adriana Verges

Description: Seagrass ecosystems are an important nursery for many species of local, commercially important fish species. However, they are sensitive to change. This project will explore the feeding behaviour and bioturbation effects of the Southern Eagle ray, a large ray species common on Australia's East coast, on shallow water seagrass ecosystems. Drones will be used to measure ray abundance, diel feeding patterns and any fluctuations in seagrass area covering. The results will provide foundational data on Southern Eagle ray behaviour in shallow water environments, and the effects of their bioturbation on New South Wales' vital seagrass ecosystems, with possible implications for the longevity and management of these areas, and therefore the commercial fisheries that rely on these crucial nursery areas for their stocks.

Experience: Drone operation is helpful but not essential. Should be comfortable around water and be a strong swimmer.

Project Title: Photo Identification and behavioural observation of Critically Endangered Grey Nurse Sharks in Bushrangers Bay

Supervisor(s): Ian Suthers, Adriana Verges, Tracy Rogers, Tracy Ainsworth

Description: The Grey nurse shark is an enigmatic species of coastal shark that are critically endangered on Australia's Eastern coast. Each individual has a unique pattern of spots, which act as an identifying feature between individuals. The aggregation site in Bushrangers bay is unique, given the presence of large sharks in shallow water, allowing for efficient assessment of behaviour and identification from snorkelling and freediving. Given grey Nurse sharks are segregated by sex for much of the year, Bushrangers bay is mainly comprised of female, likely pregnant, sharks. Therefore, cataloguing these keystone individuals through photographing their unique patterns and making observations of their behaviour is vital for population management, especially given this species extremely slow reproductive rate. This project will increase knowledge of grey nurse shark population dynamics and social structure, and will increase knowledge through the cataloguing of the most important breeding individuals and therefore aid conservation efforts and management programs for this species.

Experience: Must be a strong swimmer and capable of operating a simple camera. An understanding of shark behaviour is also necessary.

Project Title: Studying broad scale adaptations with historical wood collections

Supervisor(s): Daniel Falster

Description: Historical wood collections offer an exciting opportunity to study adaptation of plant wood to climate. This project will use historical collections of wood blocks and slides to understand variation in traits related to drought risk, including vessel size and wood density.

Experience: Suits a 3rd year student, with experience in plant ecology and data analysis

Project Title: Characterising the components of a growth ring in Mulga (Acacia aneura) for dendrochronology

Supervisor(s): Assoc Prof Daniel Falster and PhD Student Ashleigh Ford

Description: Trees put on growth rings throughout their lives in response to environmental signals. Dendrochronology—the study of these rings—allows us to better understand how trees grow, how long they live, and how they respond to the conditions they experience throughout various growth stages. In Australia, dendrochronology has long been spatially limited to areas with a strong seasonal signal, in which rings are most likely to be annual. However, there is increasing evidence for species possessing useful rings in previously disregarded environments. One such species is Mulga (Acacia aneura), an important semi-arid tree species, which has recently been found to have rings suitable for dendrochronological analysis. There remains a question, however, about what features characterise a growth ring in a non-traditional species such as Mulga, and how these features may change in response to differing environmental signals.

This project will involve analysing the anatomical structure of a Mulga growth ring, integrating within a larger project using carbon dating and dendrochronology to study the dynamics of growth in this species.

Experience: Suits a third year student interested in plants and keen to get hands on experience looking at plant adaptations and structure.