In this project the intern will work with an expert team from the company Xanadu, to explore new computational methods and approaches which could be helpful for optimizing hybrid calculations involving both classical and quantum computing combined together.
This project is focused on improving the theory, software and scope of applications of quantum computational chemistry. The intern will work with the company research team to further develop new theoretical methods which are under development, implement them into code as part of the existing open-source PennyLane package, and run tests for modelling the vibrational properties of a specific sample molecule. The intern will gain valuable experience working with a leading company in the field of quantum computing and will be able to lead the preparation of a journal-ready article.
The battery is considered as the source of power for electric transport. The performance of the battery drops at low temperature which reduces the mileage of Electric Vehicle (EV). This issue is hindering the widespread adoption of EV in cold places like Canada. The Low-Temperature Battery (LTB) can be used in EV to solve the low milage problem in extreme cold temperature, but its cost is around three times higher than the Normal Temperature Battery (NTB). So, using the LTB in an EV is not economically feasible.
academic sphere, and limit interactions with the stakeholder community. This project promotes meaningful collaboration for the co-construction of knowledge around Parkinson's disease (PD), using the performing arts as an interactive tool for knowledge translation. Piece of Mind brings together researchers, performing artists, and persons affected by PD to create a performance piece based on scientific research and lived experience.
For this project, the intern will be assessing the ability of the Kinetyx Inertial Measurement Unit (IMU) to measure 3-dimensional foot orientation and running gait metrics in real time. Measurements will be collected both in and outside of the laboratory setting, in order to establish measurement validity for a variety practical applications. The insole imbedded IMUs will be validated against the Vicon optical motion capture system as well as the Bertec force plate treadmill system within the laboratory.
Optimizing an integrated virtual care technology solution to expand capabilities to manage patients with cognitive impairment. These enhancements will improve remote monitoring and management of patients with Alzheimer’s and Dementia. The project will result in the development of additional tools and proprietary applications to enable comprehensive assessments in the areas of stroke and other neurological conditions.
Solid-state nanopores are tiny holes in thin membranes comparable to the size of individual biomolecules like protein or DNA. They are using as sensors to detect these molecules one at a time, and have the potential to revolutionize DNA sequencing, personalized medicine, point-of-care diagnostics, and next-generation information storage methods, but have not been able to so far due to the high cost and inability to scale of the methods traditional used to fabricate them. We recently developed a method that allows to inexpensive, scalable fabrication of nanopore sensors.
The primary objective of the lab demo system is to interface the FBGs sensors for monitoring of electrical parameters of a demo power grid network equipment prototyped in the lab and the second objective is to interface, multiple electrical parameters of a demo power system grid network over a single module of FBG sensor and send it remotely for the performance monitoring and control purpose.
Graphene is the thinnest, lightest, strongest, and a highly conductive material discovered to date, which makes it attractive for diverse applications, ranging from energy storage devices, electronics and automotive to construction. Despite the unique properties of graphene and its potential application in various industries, the widespread application of graphene is still limited due to the high cost of starting materials, high production cost, or low production volume.
Unsupervised machine learning has recently been introduced into the field of quantum many-body physics. A strategy based on generative models has been particularly successful in the data-driven learning of quantum states. In this proposal, we aim to adapt this technology to applications in quantum chemistry. The primary focus of this research will be on the reconstruction of molecular wavefunctions using data obtained from qubit-based quantum simulators, such as superconducting circuits or trapped ions.