Following the success of mathematical and statistical modelling in various financial markets, we believe that quantitative methods can also be used to effectively establish trading vehicles for power and its derivatives. However, most of the quantitative literature in power markets is focused on specific aspects primarily from the perspective of load-serving or generation units. Instead, we aim to build a quantitative power trading framework which expands the activities of Plant-E Corp in North-American power markets and fills in the current gaps within the literature.
The subject of assessing credibility is not new. What we are proposing in this project, on the other hand, is novel. It has been shown in various context that assessing credibility is extremely difficulty. In this project, we take a data-driven approach, relying on fundamental knowledge of neural physiology and data collected by NuraLogix, our industrial partner. The idea is simple, lying is stressful and it triggers uncontrollable neural activities that lead to subtle changes in physiological processes, which can be measured and analysed.
The complex, turbulent features of fast-flowing tidal currents at the FORCE tidal energy site in Minas Passage make it difficult to accurately measure, model and predict the hydrographic features of the flow. But, these flow predictions are critical to effectively assessing the impact of tidal stream energy on marine life. This project aims to improve the understanding eddies, waves, wakes and other flow features at the FORCE site.
Due to the potential for significant cost-savings, many companies are turning their attention to digital simulations which produce an enormous amount of data. For companies to realize the benefits of having access to this data they need tools that allow efficient and accurate extraction of information from the data set. The goal of this project is to conduct the research required to develop a framework for the implementation of machine learning algorithms to provide engineering predictions for industrial applications.
Quini is developing a revolutionary system that allows wine producers to predict with a high level of accuracy how much acceptance and sales they will be able to generate from a wine product, over time, in which major cities and selling to whom as the primary buyers. The system will also give consumers exacting wine recommendations that suit their personal taste and that are likey to be available for purchase in their area.
We will design and build a software suite for producing 36-hour forecasts of water flow rates and turbidity (cloudiness) at any point along the channels of the Upper Yellowstone River Watershed (UYRW). This forecasting system will respond to real-time sources of information on weather and upstream sensor readings, and will be designed for continual updates (via automatic daily recalibrations) based on modern statistical techniques.
As the COVID-19 pandemic has made painfully clear, it is both important and difficult to analyze the large volumes of patient data collected by hospitals and other healthcare providers. Ideally, data would be widely-shared between institutions, and experts and teams with diverse backgrounds would be able to contribute to the analysis. Unfortunately, this is not possible: sharing of healthcare data would severely compromise patient privacy, with many negative consequences.
MITACS interns at 1QBit will aid in the research and development of experimental usage of quantum and classical hardware devices for industry use including, healthcare, finance, advanced materials, and optimization. Interns will have the opportunity to work alongside academics and research teams. MITACS interns will gain the practical experience of applying their knowledge for industry use and working in a business setting with clients. Internships provide a great opportunity for future career of the students at 1QBit.
Under the current pandemic of Covid-19, sharing health record data has tremendous benefits to control the spread of the infection and save lives globally. In medical research and discovery, Electronic medical records (EMRs) play the essential role for medical discovery in two categories, namely 1) cross-sectional study and 2) longitudinal study. Cross-sectional study compares different population groups at a single point in time while in longitudinal study, researchers conduct several observations of the same subjects over a period of time.
Leveraging the entirety of point of sale and loyalty data collected across a category, as well as additional socio-economic and other supporting data sources, apply statistical modelling to identify the own-price elasticity of demand and cross-price elasticity of demand at regular and promoted price points across Unilever’s portfolio within that category. Subsequently measuring the promotional cannibalization of Unilever’s temporary price reduction activities across the market to assess the promotional events with the highest return on investment and revenue optimization potential.