Summer research experience program

Applications for Summer 2020–21 are now closed.

The BME Summer Research Experience Program is a 10–12 week (nominally December to February) voluntary placement to provide students meaningful work experience through observation and skill development in a research laboratory.

The placements aim to assist students to inform future career choices perhaps undertaking a PhD or working in laboratory positions in the future.

Students will have the opportunity to undertake work experience with academics and research staff in the Department of Biomedical Engineering to gain valuable exposure and experience in research. A limited number of placements are offered and are allocated on a competitive basis. Successful applicants are expected to commence in early December.

These placements are unpaid work experience opportunities and are undertaken voluntarily by the student. Students and supervisors will agree on hours of the placement however typically this would be around 5–10 hours a week to enable a meaningful learning experience.

This program does not replace or provide academic credit for any subjects. The continuation of this program is subject to Government and University restrictions related to the global COVID-19 pandemic.

  • Eligibility criteria

    The placement is available to students studying Engineering, Science, Medicine and related disciplines.

    The applicant is a student who is:

    • A high-scoring students (H1 level equivalent)
    • Either:
      • Commencing any year of their Master’s degree in 2021; or
      • Commencing the final year of their Bachelor Degree in 2021.
  • How to apply

    If you meet the eligibility criteria, please complete the following steps to apply:

    1. Write us an email containing the following information:
      • Student number
      • Student email address
      • Current postal address
      • Phone number
      • Your top three projects in order of preference
      • Why you are interested in undertaking the program and what your plans for further studies are
      • Attach Statement of Results to date
    2. Email this to nbaxter@unimelb.edu.au from your student email account.

Placements

The following placements have been identified for the Summer Research Experience Program this year. At the end of these placements the student will have gained skills that are required to operate within an engineering laboratory, an understanding of key processes and procedures and an understanding of the options for specialisation within the placement discipline.

  • Development of finite element models of subchondral bone

    Supervisor: Fatemeh Malekipour

    In this placement the student will engage with a team of engineers and veterinary surgeons who are developing models that help understand the fatigue injury in the joints of racehorses. We have performed mechanical testing on the high-risk areas within the joints, ie, subchondral bone. The student will participate with the team in developing finite element models of these areas to further determine the role of biomechanical parameters in the joint injury. The student will become familiar with image processing and finite element software and gain understanding of the biomechanics of subchondral bone at the joint.

  • Measurement of microfluidic flow rates via the electrical double-layer

    Supervisor: David Collins

    Measurement of low flow rates has been a longstanding challenge in biomedical devices, especially in a cost-effective way. In this placement the student will gain an understanding of the process of developing a unique flow-sensing concept based on electrical capacitance that has the potential to provide greater sensitivity at lower costs than previously possible. Students with an interest in bench-scale lab work, simulations and mathematical modelling are encouraged to apply.

  • Decoding neural activity in the visual brain

    Supervisor: Hamish Meffin

    In this placement the student will learn skills in modelling and analysis to investigate how neurons in the visual cortex encode information about visual imagery. We have made recordings of neural spiking responses from visual brain areas during viewing of visual images. We noticed that sometimes neurons respond to images with a single spike and at other times they respond with distinctive bursts of spikes. We hypothesis that single and burst spiking may be used by neurons to encode information about distinct visual features, similar to switch. In this placement you will understand how this hypothesis can be tested by the application of advanced mathematical techniques to identify which visual features a neuron is responding to during single and burst spike events. This is expected to deepen our understanding of how the brain processes information.

  • Upper limb motion analysis using wearable sensors

    Supervisor: David Ackland

    Human movement disorders affect one-third of Australians; however, conventional approaches to assessing joint motion are costly and largely clinic- or laboratory-based. Our research aims to use biomechanical modelling to non-invasively produce accurate, low-cost, user-friendly shoulder and elbow joint angle measurements using wearable inertial sensors. In this placement, the student will understand how measurement of upper limb motion on healthy subjects is be carried out in our state-of-the-art Motek CAREN human motion analysis laboratory. Accurate, wearable motion measurement will benefit next-generation healthcare including telemedicine and remote rehabilitation for isolated communities, and performance monitoring of elite athletes. This placement would most benefit a student with basic background in biomechanics, who have taken BMEN30005 Introduction to Biomechanics. An interest in, and experience with MATLAB will also be considered

  • Development of a neural spike simulation suite

    Placement Supervisors: Katie Davey, Artemio Soto-Breceda

    This placement focusses on skills in computational neuroengineering and would suit a student with an interest in applied MATLAB programming skills. The student will learn how a neural spiking simulation suite is developed with a graphical user interface (GUI). The GUI will allow a user to configure a number of parameters. Based on the user’s defined cell parameters (neurotransmitter, soma size, cell type, etc) the student will gain an understanding of how simulation of the membrane potential and spiking behaviour of each neuron occurs, plus local extracellular dynamics. The activity of multiple neurons are then superimposed (added together) to simulate an electrode positioned near a number of neurons that is acquiring the local voltage, which all local neurons may contribute to. We already have spike extraction software that can then separate out the spikes of individual neurons from the voltage signal. The simulation GUI is being developed to enable us to test our spike extraction algorithm.

  • Use of a neuromorphic camera to simulate neural activity

    Placement Supervisors: Katie Davey, Artemio Soto-Breceda

    This placement is in computational neuroengineering and would suit a student with strong maths and Python (or Matlab) programming skills. It is known that the human eye transmits event-based information rather than frame-based information, resolving issues of data redundancy and latency. Neuromorphic cameras are biologically inspired vision sensors that respond only to changes in illumination rather than to illumination itself, thereby alleviating the bottleneck induced by data redundancy in frame-based systems. In this placement the student will learn how to use a neuromorphic camera as input to a spiking neural network (SNN) to simulate the generation of simple receptive fields in the visual cortex. Code for development of frame based receptive fields has already been written and will be extended to enable development of receptive fields using spiking events and incorporating temporal information for the development of temporal receptive fields.

  • Further development of a novel platform for testing tissue-engineered constructs over time

    Supervisor: Kathryn Stok

    Felix is a custom-built testing machine allowing simultaneous capture of microscopy images and mechanical properties of tissue-engineered materials. In this placement, the student will work with our team to make incremental design improvements to Felix, including new testing modes, improved biocompatibility, and an updated user interface. Students with an interest in hands-on lab work, mechatronic design, and materials testing are encouraged to apply.

  • Investigating rapid flow mixing in a microfluidic device

    Supervisor: David Collins and Daniel Scott

    Cell cultures can be used to elucidate the effects and mechanisms of novel pharmaceutical treatments before animal and human trials. Most techniques for cellular analysis, however, are predicated on measuring the time-averaged cellular responses, rather than those occurring over shorter time scales. Neuromodulator signal transduction, for example, can occur on the order of milliseconds. There is therefore a need to develop approaches that can rapidly introduce drugs and measure subsequent cellular responses. In this placement you will learn how to develop a geometry that can rapidly different fluid volumes in order to measure these millisecond-order cellular responses. This placement will have an emphasis on computational modelling in order to predict the behavior of fluids in passive mixing geometries.

Further information

Email nbaxter@unimelb.edu.au