Postdoctoral fellows receive Banting Postdoctoral Fellowships

Learn more about Benjamin's research

Advanced quantum state generation for on-chip quantum repeaters towards long-distance fiber quantum communication.

Abstract:

The internet has transformed our lives in unforeseen ways. We are now seeing the rise of a new type of network that could potentially be as transformative, the quantum internet. This technology will ensure the privacy and security of communications for the government, healthcare, and financial sectors through the fundamental laws of quantum mechanics. Beyond communication, it will enable networks of quantum computers and quantum sensors to accelerate progress in many key applications like traffic management, supply chains optimization, drug and battery development, high-precision GPS, climate change modelling, and monitoring gravitational waves towards a deeper understanding of the origin of the universe, along with many. However, the reach of quantum signals, carried by barely a handful of photons, is severely limited since it is physically impossible to copy a quantum state without corrupting its fragile intricacies like superposition (‘cat both dead and alive’) and entanglement (‘spooky action at a distance’).

To solve this problem, there has been a sustained effort to develop a quantum repeater, which would overcome link losses by sharing entanglement along multiple nodes along the link. However, practical quantum repeaters remain undemonstrated due to challenges with reliable quantum memories and other hardware and software issues. Here, a recent proposal for a different repeater design avoiding such memories will be experimentally implemented. It will rely on entangling multi-photon entangled states in both their time-of-arrival and frequency, creating a rich resource for quantum information processing. Leveraging the precision and reliability of silicon photonics, advanced by the electronics industry, will enable the creation of large-scale quantum circuits for all-photonic repeaters. This project aims to develop these repeaters to unlock the potential of quantum networks on a national and global scale.

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Does the increase in lactate promote the health benefits of exercise?

Abstract:

Exercise has many health benefits and can reduce the risk of dozens of chronic diseases. The effects of exercise to reduce levels of inflammation in our bodies is most likely a key reason why exercise reduces the risk for so many chronic diseases. For many years scientists have been searching for a “magic” factor that explains how exercise can have such widespread health benefits. We believe that lactate, a molecule produced by exercising skeletal muscles, might be this magic “exercise factor”. The purpose of this research is to determine if lactate can exert anti-inflammatory effects to help explain the health-promoting effects of exercise in humans. We will use both cell culture studies and experiments with human volunteers performing exercise to help us understand what lactate does to the levels of inflammation in our bodies. This research will help us understand how exercise works to have so many health benefits and determine if lactate is an “exercise factor” that signals from our muscles to our immune system during exercise.

Learn more about Hossein's research

Combined AI and Network Modeling of Non-Newtonian Flows in Porous Media

Abstract:

Porous materials are substances that have tiny holes or spaces within them, allowing liquids or gases to pass through. This research focuses on studying how various complex fluids move through porous materials by developing artificial intelligence (AI) and network models. Traditional models have effectively described simple fluids, like water, which flow consistently regardless of the applied force. However, many natural and industrial fluids, such as blood, polymers, and slurries, behave differently. These fluids, known as non-Newtonian fluids, change their flow characteristics under different stresses, making them harder to predict and model. Understanding these complex fluids is crucial for applications in groundwater management, medical diagnostics, drug delivery, and materials processing.

Traditional methods often fail to capture the intricate behaviours of these fluids within porous structures. For example, certain non-Newtonian fluids can create specific pathways while blocking others, affecting the porosity of the material. Our research aims to overcome these challenges by combining AI with network modelling. AI will help predict and analyze the flow behaviours of non-Newtonian fluids using physical data, while network modelling will simulate the detailed interactions within porous materials. This innovative approach promises to improve the accuracy and efficiency of flow predictions, leading to significant advancements in both our theoretical understanding and practical applications. The results of this research will benefit various fields and industries, including environmental protection, healthcare, and manufacturing, enhancing Canada's scientific and technological capabilities. For example, the knowledge gained can aid in protecting water resources from surrounding contaminants, reducing carbon dioxide and methane emissions by effectively sealing repurposed reservoirs, and developing enhanced and targeted drug delivery methods, among other applications.

Learn more about Xu's research

How New Species Arise: Insights from the Genomic Study of Sunflowers

Abstract:

Speciation in the presence of gene flow poses a significant challenge to traditional evolutionary theory, which posits that gene flow hinders the divergence necessary for new species to arise. An emerging solution involves recombination modifiers, which suppress recombination between genes involved in local adaptation and/or those contributing to reproductive isolation. This research aims to elucidate the genomic and selective mechanisms that enable speciation despite gene flow, with a particular focus on the role of recombination modifiers such as chromosomal inversions and DNA methylation in wild sunflower species (Helianthus). By characterizing 37 large non-recombining regions identified in three diverging wild sunflower species, this study will explore the establishment and evolutionary roles of these regions. Utilizing high-quality reference genomes and resequencing datasets, the study will determine the genomic basis of these regions, assess their distribution across the genus, and investigate their establishment mechanisms. Bisulfite sequencing will be employed to understand how variations in DNA methylation influence recombination rates. This integrative approach, combining genomic, epigenetic, and environmental data, aims to advance our understanding of speciation with gene flow and provide insights into the origins and conservation of biodiversity.