A limited number of fully funded fellowships are available. However, Mitacs strongly recommends that you confirm the availability of $5,000 per year from your academic supervisor or university before applying.
Fellowships will be awarded competitively.
Vibration analysis is probably the most widely used technique to perform health monitoring of mechanical machinery. Specifically, we are interested in monitoring ‘Vibrating screens’, machines that are for example used by the mining industry to sort aggregate by size. Over the last 10 years the research group of Dr. v. Mohrenschildt has developed hardware, software and theory to accomplish this. The goal is to further the understanding of feature extraction and classification to perform effective predictive maintenance.
Innovative approaches that ensure food security in light of the increasing world population, increasing variety of crop pests and microbes, and accelerating climate change are urgently needed. Suncor has developed a novel plant immune aid that can effectively enhance the disease resistance of crops to enhance agricultural yields. Through this collaboration with Dr.
Legionnaires is a disease caused by the bacteria Legionella pneumophilia present mostly in aquatic environments. The first outbreak of this disease was recognized in 1976 in Philadelphia and the most recent one in July 2019 in Atlanta. Diagnosis of the disease isn’t early and thus need to be prevented by regular treatment. Treatment of water needs information about the water quality which needs on-site based sensors to detect the different pathogens present. At present, there is no viable solution to detect Legionella on site with confidence.
Railway tank cars are constructed from TC 128 steel plates, a design that has not changed for more than 50 years. The Lac Megantic rail disaster in 2013 refocused the attention of Canadians on the safety aspects of tank car design and operation, but not so much on the actual properties of the steels used to build them. In this project, we will explore the potential of modern advanced high strength steels as a replacement for TC 128. In particular, significant improvements to the tank wall puncture resistance will be targeted. New alloys will be designed using the latest scientific knowledge.
The ultimate goal of this project is to develop a fundamental understanding of inclusion evolution during a particular refining process in secondary steelmaking unit. The particular focus is firstly on developing a detailed characterization of the inclusions formed during refining in the Stelco Ladle Metallurgy Facility, and secondly on adapting the existing McMaster ladle metallurgy/inclusion model for the Stelco facility. Ultimately this is expected to achieve better process and product control. Inclusions, depending on their size and type, may profoundly affect steel properties.
The Ladle Metallurgy Furnace is used for adjustment of chemical composition and temperature, and control of tiny particles called “inclusions”. Controlling inclusions is carried out by adding calcium to modify the solid alumina or magnesium aluminate inclusions to less harmful liquid inclusions.
During ladle process, reaction of top slag, steel and inclusions occur simultaneously. Therefore, establishing a model to describe ladle process is indeed a challenge.
Object detection and classification for surveillance applications via deep neural networks have attracted a lot of interests in computer vision (CV) communities. Accurate and fast CV algorithms can alleviate intensive manual labour and reduce human errors due to fatigue and distraction. In detection problem, the aim is to determine bounding boxes which contain interested objects and classify the category of the detected object. Thus, the detection problem can be formulated as a regression problem to localize multiple objects within a frame.
As part of this proposal the intern will be working with DME to develop and examine the viability of data-driven point-of-care system for the assessment of suicide risk. DME is a Canadian start-up company in the business of developing cloud-based point-of-care monitoring systems for the management of psychiatric illnesses. DME has developed algorithms to diagnose and predict optimal treatment for major depression disorder and schizophrenia, and has been allowed patents describing its technology in Canada, the USA, and Australia. If found acceptable the algorithms