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“If you are interested in applying computational biology to real-world applications that could potentially lead to publishable research work, this is the right opportunity for you. Be a part of the team of research and machine learning scientists and get mentored by some of the best minds in AI.”
- Shazan Jabbar - Lead Machine Learning Scientist and Mara Cairo, Product Owner - Apprenticeships
One of Canada’s three main institutes for artificial intelligence (AI) and machine learning, our world-renowned researchers drive fundamental and applied research at the University of Alberta (and other academic institutions), training some of the world’s top scientific talent. Our cross-functional teams work collaboratively with Alberta-based businesses and organizations to build AI capacity and translate scientific advancement into industry adoption and economic impact.
In this project we propose to start by building on two current approaches for predicting relative biomolecule abundance that have shown promise in other species and for which NRC-generated data is available or planned. The approaches that are identified to investigate further are: 1) Ortholog contrast models and 2) Massively parallel reporter-based models. Both of these approaches take advantage of the recent advancement in deep learning models. NRC data will be limited to sequence information from plant protein crops, specifically canola and pulses (peas, lentils), with priority given to seed-related traits. As a first step, the above existing approaches will be validated by reproducing the published results. Then those calibrated models will be applied to NRC data. Following this demonstration, the focus will shift to ways to improve the model performance. Possible avenues to explore here would include feature engineering, data expansion, transfer-learning, and addressing data-quality issues.
The outcome of this project will be a first generation of predictive models of molecular phenotype (aka endophenotype) abundance. These models are expected to have wide ranging utility for crop improvement. By providing a means to elevate trait associations and modelling to a functional level (i.e. transcript, protein or gene/functional dosage), these types of models are expected to drive a paradigm shift in breeding and trait development.
The project will be executed across a two-year time period, and Amii will onboard a number of consecutive interns over the course of the project. The interns will work under the supervision of an Amii Lead Scientist for the duration of their internship. Internships start at 4 months with the possibility of extension of up to 12 months.
We’re looking for a talented and enthusiastic Intern with a strong knowledge of computational biology and machine learning.
Besides gaining industry experience, additional perks include:
If this sounds like the opportunity you've been waiting for, then please don’t wait to apply! Please send your resume and cover letter indicating why you think you'd be a fit for Amii by December 1 through the Indeed listing.
Applicants must be legally eligible to work in Canada at the time of application.
Amii is proud to be an equal opportunity employer. We are committed to creating a diverse, inclusive and excellent workforce.
Jul 24th 2024
News
How do we get the best results when AI and human beings work together? In this episode of Approximately Correct, we’re looking into Human-In-The-Loop AI with Amii Fellow and Canada CIFAR AI Chair Matt Taylor.
Jul 22nd 2024
News
Read our monthly update on Alberta’s growing machine intelligence ecosystem and exciting opportunities to get involved.
Jul 18th 2024
News
Amii announces work with Communitech, a Waterloo Region innovation hub, to empower startup founders with the AI tools and resources they need to integrate AI and build in-house capabilities successfully. The collaboration will leverage Amii’s leading AI expertise and resources and be centred around Amii’s Machine Learning Exploration (ML Exploration) program.
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