Algorithmic principles of navigating the brain using nanorobots

Summary

Over the past several years, high-throughput neuroscience methods have yielded comprehensive cellular maps of the entire brain. These data have revealed features, like distinctive marker cells and low-dimensional molecular patterns, that could be exploited by nanoscale devices to enable efficient navigation. At the same time, nanoscale sensing devices, such as those fabricated using DNA origami, are becoming increasingly sophisticated in their ability to perform useful computations. Accurate nanoscale navigation would enable transformative new treatments of brain diseases and cognitive therapies via targeted drug delivery.

Supervisor(s)

Dr Ben Fulcher

Research Location

School of Physics

Program Type

PHD

Synopsis

In this project, we will perform new physics-based characterizations of molecular patterning in the brain, and use physical simulations to investigate how nanoscale sensing rules can efficiently navigate to a given target location in the brain. The student will work with whole-brain neuroscience datasets, and use methods from statistical learning and physics to develop optimal sensing rules required for accurate and efficient brain navigation. This project is supported by a diverse team as part of the School of Physics Grand Challenge in Nanoscale Brain Navigation.

Additional Information

The additional supervisor for this project is Dr Shelley Wickham.

Excellent facilities are available to carry out all aspects of the work, including access to computing resources, cellular brain maps, and, where required, a diverse supporting team ranging from Engineering, Neurobiology, and Chemistry. The student should have a strong interest in mathematical modeling (with a quantitative background in e.g., physics, mathematics, statistics, engineering, or computer science) and enjoy working as part of a vibrant interdisciplinary team. Top-up funding is available for the highest quality of applicants, with additional funding available to support travel to present research results at national and international conferences and to visit collaborators. 

HDR Inherent Requirements
In addition to the academic requirements set out in the Science Postgraduate Handbook, you may be required to satisfy a number of inherent requirements to complete this degree. Example of inherent requirement may include:

  • Confidential disclosure and registration of a disability that may hinder your performance in your degree;
  • Confidential disclosure of a pre-existing or current medical condition that may hinder your performance in your degree (e.g. heart disease, pace-maker, significant immune suppression, diabetes, vertigo, etc.);
  • Ability to perform independently and/or with minimal supervision;
  • Ability to undertake certain physical tasks (e.g. heavy lifting);
  • Ability to undertake observatory, sensory and communication tasks;
  • Ability to spend time at remote sites (e.g. One Tree Island, Narrabri and Camden);
  • Ability to work in confined spaces or at heights;
  • Ability to operate heavy machinery (e.g. farming equipment);
  • Hold or acquire an Australian driver’s licence;
  • Hold a current scuba diving licence;
  • Hold a current Working with Children Check;
  • Meet initial and ongoing immunisation requirements (e.g. Q-Fever, Vaccinia virus, Hepatitis, etc.) 

You must consult with your nominated supervisor regarding any identified inherent requirements before completing your application

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Keywords

nanoscience, brain navigation, physical modeling, Machine learning, brain mapping, brain disease, nanorobots

Opportunity ID

The opportunity ID for this research opportunity is: 2912

Other opportunities with Dr Ben Fulcher