Integrating Deep Learning and Machine Learning Techniques for Maize Yield Monitoring with Earth Observation and Climate Data to Ensure Food Security in Dry Regions

Our project aims to improve maize production efficiency and mitigate the impacts of climate change. By combining advanced computing, artificial intelligence, and remote sensing techniques, we will analyze data on maize cultivation, climate patterns, and soil health. This collaboration between institutions in both countries seeks to enhance agricultural sustainability, increase food security, and contribute valuable […]

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Re-inhabiting Lives, Uncomfortable Histories: Armenian Women Immigrants in Turkey

The research I wish to pursue for my research in Turkey seeks to explore following questions: How are the subjectivities of undocumented Armenian women immigrants shaped in the face of multi-layered discrimination and exclusion? What is the role of Turkish media and Turkish cinema in this discrimination? My inquiry into the ways in which undocumented […]

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Mathematical modeling of vertebrate retinal cone photoreceptor patterns

Cone photoreceptors are specialized cells in the retinas of vertebrates that absorb light to permit daylight vision. Two major morphological types exist: single cones, which are circular in cross section, and double cones, which consist of two apposed cones with elliptical cross section. In many vertebrates, cones are organized into repeating, lattice-like mosaics. The two […]

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A data-driven control framework for constrained nonlinear systems with application to unmanned ground vehicles

The objective of this project is the development of a novel data-driven control framework for nonlinear dynamical systems. The proposed solution will be validated and tested on the autonomous ground vehicles available at Concordia University. To achieve the desired goal, the research intern, with the supervision and help of the host and home supervisor, will […]

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Computational modelling of advanced electrocatalysts for CO2 electroreduction using DFT and machine learning.

Carbon capture and utilization (CCU), primarily the CO2 electroreduction technology, can convert CO2 into a variety of valuable products, using renewable electricity. However, the path to widespread adoption of the CO2 electroreduction technology in industrial settings is met with several challenges primarily the cost of electricity, efficiency and selectivity of the desired product. One of […]

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Advancing Systems Architecture Development Methods for Aircraft with Hydrogen-based Propulsion

Ensuring the sustainability of air transportation is a priority for the global aerospace community. Disruptive technologies, such as hydrogen-based propulsion, are promising but present significant challenges for the design and operation. One challenge for designing these future aircraft is the large number of potential architectures and the need to consider safety already in the early […]

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Bimetallic Cooperation: Design of an Fe–Al Complex for Carbon Dioxide Activation

The proposed project aims to address the pressing challenge of reducing greenhouse gas emissions by leveraging synthetic chemistry. By designing a novel iron-aluminum complex for carbon dioxide activation, the project explores bimetallic systems to introduce innovative pathways for reducing carbon dioxide’s harmful environmental impact. The participating institutions, Imperial College London and the researcher’s home institution […]

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Development of a Comprehensive Neighborhood-based Measure of Cardiovascular Health in Children

Promoting healthy living and reducing the burden of cardiovascular diseases are public health priorities in Canada and the United States. In recent years, the importance of measuring neighborhood contexts to address inequities in cardiovascular health has been highlighted. Many neighborhood indices have been utilized by academic researchers to explore the complex relationship between neighborhood and […]

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Automated Software Vulnerability Patching using Dynamic Symbolic Traces

Deep learning (DL) has emerged as a viable means for identifying software bugs and vulnerabilities. The success of DL relies on having a suitable representation of the problem domain. However, existing DL-based solutions for learning program representations have limitations – they either cannot capture the deep, precise program semantics or suffer from poor scalability. We […]

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