D2K+: Deep Learning of System Crash and Failure Reports for DevOps

The objective of this project is to develop techniques and tools that leverage artificial intelligence to automate the process of handling system crashes at Ericsson, one of the largest telecom and software companies in the world, and where the handling of crash reports (CRs) and continuous monitoring of key infrastructures tend to be particularly complex […]

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Synthesis of Thiol-PEG-PBS-grafted gold nanoparticles for the treatment of head-and-neck cancers

Cancer represents a major public health problem, representing about 13% of deaths worldwide, accounting for more than seven million deaths a year. Surgical excision, chemotherapy, and radiotherapy encompass the forefront of antineoplastic therapy; however, it is well known the numerous adverse effects related to these therapeutic modalities. Some treatments have enough potential to help or […]

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Development of an NLP Sales Assistant using Machine Learning Techniques

The main goal of this project is to develop machine learning and natural language processing approaches to help customers to communicate their preferred brands and/or retailers via Heyday solutions. These approaches will automate answers and help to humanely engage with customers. In order to reach these objectives, some challenges will be tackled such as automatically […]

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Bond Pricing AI Improvement

The fixed-income market consists of government and corporate bonds and other debt instruments which are used to finance operations and capital investments. The bond market remains heavily reliant on exchanges of information between counterparties and as a result information on prices is decentralized and market participants operate with different levels of information. The objective of […]

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Développement de composantes logicielles intelligentes pour la gestion des flux de trésorerie

Le projet vise à développer de nouvelles composantes intelligentes pour un logiciel financier. Ainsi, différentes problématiques devront être résolues à l’aide de techniques statistiques et d’apprentissage machine et profond. Les données disponibles étant principalement du texte, le projet nécessite une étape de transformation afin de rendre les données utilisables dans les modèles appropriés. Des modèles […]

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Development of new enzymatic products for optimization of paperproperties.

number of industrial processes, as well as on papermaking productivity. Such impacts have to be considered too in the emerging field of biorefining (production or extraction of high value products from forest biomass). The main partner to this project (Buckman Canada) has successfully introduced enzymes to the paper industry, but would like to expand its […]

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Design and development of techniques to characterize optical, mechanical and chemical properties of metallic and semiconductor thin films with applications in MEMS structures and their packaging

Micro-Electro-Mechanical Systems (MEMS) are complex systems with sizes in the range of few microns (human hair has thickness of 150-200 microns) which have both mechanical and electronic components. MEMS technology has entered in many industries such as optical technology, point of care diagnostics, telecommunications, automotive, and military. Today, there are hundreds of MEMS devices, e.g. […]

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Modeling the risk of breakage of beaver dams

Municipalities in Quebec lack tools to effectively anticipate and manage the consequences of beaver dam outburst flooding. When beaver dams fail the resulting flooding can pose a major threat to public safety and public and private infrastructure. A research project lead by Dr. Jan Franssen in partership with OBV RPNS and its Municipal partners is […]

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Intelligent Cyber-Physical Situational Awareness for Smart Infrastructures

The availability of big data in smart infrastructures have become a strategical asset for operators to understand the situation of the infrastructure and monitor potential threats. However, most of the data still have not circulated beyond traditional corporation and technological boundaries, which have limited the visibility that could have been provided by the abundant data. […]

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Self-Adaptive Penetration Tests with Deep-Reinforced Intelligent Agents

Penetration testing is a key security tactic, where defenders thinks like an attacker to predict the latter’s actions and develop effective defense. However, for large-scale cyber-physical infrastructures like the smart grid, traditional penetration tests on individual devices or networks are insufficient to exhaust all potential exploits or to reveal infrastructure-level vulnerabilities invisible to the local […]

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Data driven energy efficient base station sleep control for 5G systems

The objective of this project is to develop a software system which can optimally control the base station sleep states in 5G networks to save energy. The 5G wireless networks are required to be green and yield very low carbon dioxide emissions. Compared with that of 4G wireless networks, the power efficiency of 5G is […]

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Transparent and Trustworthy Deep Feature Learning for Cyber-Physical System Security

The latest artificial intelligence (AI) technologies have effectively leveraged the wealth of data from cyber-physical systems (CPSs) to automate intelligent decisions. However, for safety-critical CPS like smart grids and smart cities, the conversion of massive data into actionable information by the AI must be not only effective but also reliable. To this end, this project […]

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