Implantable medical devices such as brain machine interfaces (BMI), deep brain stimulators (DBS), vision prosthesis and cochlea implants use electric pulses to stimulate the central nervous system. These electronic devices have restored hearing to the deaf, functional sight to the profoundly blind, alleviate symptoms of drug resistant depression and epilepsy, and allowing those with locked-in syndrome “the inability to move and speak due to brainstem injury or ALS" to communicate and move again.
The parameter space for these stimulation pulse trains are enormous, involving for example, waveform shape, amplitude, duration, inter-pulse interval and phase. The pulses used in existing devices are based on educated guesstimation, and labour-intensive trial-and-error experiments. The combinatoric design space cannot be adequately nor efficiently addressed by such traditional, low-throughput technique. In this project you will help develop automated, high-throughput methods for finding effective stimulation waveforms."
"Electronic implants for the peripheral nervous system (PNS) have successfully restored sensory and motor functions and are viable therapeutics for epilepsy and depression. But current devices have many shortcomings. We will develop mm-scale, wireless, and soft neurotechnology, capable of concurrent stimulation and recording in the PNS. Our devices will enable bidirectional neural interfacing with improved performance, stability, and reduced invasiveness.
This project will help to develop mm-scale power harvesters of acoustic energy, delivered via ultrasound transducers. The energy collected will be used to power wireless, miniature neural implants placed deep inside the body. As an extension the acoustic interface could be augmented for communication, via backscatter echoes. We foresee this medical technology to drastically reduce invasiveness, by shrinking implant geometry from conventional cm-length-scale to mm-length-scale. Simultaneously it will enable efficient wireless power / data transfer over centimetres of body tissue, as opposed to the few-mm limit of traditional inductive power transfer methods, thereby enabling new classes of medical implants capable of operating deep within the body.
This project will involve laser-fabrication and benchtop testing of piezoelectric ceramics. Therefore, you should be very competent with analog circuit testing processes. We will train you on high-power laser fab."
We are investigating the use of pluripotent stem cells to make blood vessels and blood. Our research has shown that the aorta and blood stem cells require a system that mimics the circulation of a human embryo. This project looks at design of a scalable microfluidic platform for manufacturing blood vessels and blood stem cells.
This project will develop an automated microscale bioreactor to manufacture genetically modified cells for use in human cell and gene therapy. Existing large-footprint machines require skilled staff and complicated multi-step procedures, resulting in prohibitively expensive treatment costs, limiting accessibility, e.g. a cure for some cancers with CAR-T cells is possible but available to few, costing up to US$0.5 million. We have developed microfluidic technology for miniaturising and simplifying cell manufacture which reduces cost.
"Nanoscience and nanotechnology arose in the last decades at the frontline of a broad range of research fields, due to the highly challenging and unexpected properties of nanomaterials and nanosystems. These properties enable nanomaterials in various shapes (0D, 1D, 2D, 3D) and phases or nanosystems to be applied in a wide range of applications, from biomedical to industrial engineering. Furthermore, these materials frequently serve as an interdisciplinary bridge between diverse scientific and technical fields.
When dealing with the impacts of numerous processes occurring at the nanoscale, the application of machine learning (ML) techniques has become more attractive given the rising complexity of the data while enabling remarkable advancements that push forwards the broad spectrum of nanostructured materials-based technology. Furthermore, it is evident that, for such materials, theoretical formulations and ML-based solvers dealing with mesoscopic or macroscopic descriptions are required tools to improve experiments and practical investigations."
"The 3D genome organisation regulates gene expression by bringing distal regulatory elements, such as enhancers, to promoters in close spatial proximity. While many scientists have been working on cell-type specificity of gene regulation through transcriptomic sequencing,
comprehensive investigation of cell-type specificity of 3D genome conformation patterns is still lacking. Recently, single-cell methods allow us to examine cell-type heterogeneity and profiling chromosome architecture at the single-cell level has been achieved using chromosome confirmation capture (Hi-C). However, unbiased and robust computational methods are urgently needed to study cell type-specific chromosome structural patterns and accurately identify local enhancer-promoter interactions at single-cell level."
"In recent decades a significant effort has been made toward the miniaturization of sensors to replace bulky, expensive, and complex analytical instruments used in the health and environmental sectors. Different techniques such as optical, piezoelectric and electrochemical methods have been used to make devices capable of detecting analytes of interest. Significantly, the electrochemical detection method has been one of the more common techniques used in diagnostics and environmental monitoring devices due to its accuracy, cost-effectiveness, and simplicity; however, the sensitivity and selectivity of the analytes of interest require further studies.
Nanotechnology, as a discipline to understand the matter at the nanoscale dimension involves the fabrication, manipulation, study of technique, material, modes and use of nano-devices in various applications. Nanomaterial-based sensors are highly sensitive and specific in their nature as compared to traditional material-based sensors.
Genetic diseases and disorders (GDs) are usually caused by genomic variations in the DNA sequence ranging from 10% to 80%. There is, however, an important challenge in identifying responsible genomic variations. The genomic variants usually affect hundreds of genes; however, for only a subset of these variants the gene responsible for the phenotypes is known. Therefore, a computational framework is needed to provide a critical downstream analysis to prioritise long lists of genes. In this project, we will use Artificial Intelligence techniques to develop an innovative Deep Convolutional Neural Network Variants prioritisation model to detect the risk genetic variants in children with genetic diseases and disorders. We believe this innovative model will play a vital role in reducing barriers to clinical uptake of DNA testing and personalised medicine. The project will be carried using a national cohort of ~12,000 individuals, as well as ~45,000 publicly available individuals.
Artificial Intelligence, Machine learning, Genetic diseases, Rare diseases, Bioinformatics
Resources: We have access to gene expression and mutational profiles of 12,000 cancer individuals. We have also local high performance computing systems in BioMedical Machine Learning Lab.
The skills required for project(s): Scripting experience in Python, R, or MATLAB.
What skills will you gain during your degree: Machine learning and data analytics techniques, Bioinformatics, Deep learning models.
A new AI model to predict cancer-related genes/variants