Join us for a unique ML Resident role that focuses on utilizing ML/DL to improve and enhance Canadian protein crops with our partners at the NRC. Youll work with a dynamic team of machine learning scientists and domain experts building innovative models with custom genetic data.

- Dave Staszak, Lead Machine Learning Scientist and Adam Danyleyko, Product Owner, Advanced Technology

About the Role

This is a paid Residency that will be undertaken over a twelve-month period with the potential to be hired by our client afterwards. The Resident will be reporting to an Amii Scientist and regularly consult with the Client team to share insights and engage in knowledge transfer activities

About the Client

The National Research Council (NRC) is Canadas leading research and technology organization, our world-renowned research pushes the boundaries of science and engineering to make the impossible, possible. Every day we explore new ideas through innovative research and help companies discover possibilities that impact Canadas future and the world.

This projects objectives are to apply data-driven discovery to support efforts that will enhance the production of Canadian protein crops and to devise machine learning methods to mine genomics datasets generated at the National Research Council (NRC). This collaboration at the intersection of AI, genomics and plant biology will extract meaningful biological information patterns and produce valuable information for future research and application in diverse agricultural fields. Specifically, this projects primary objective is to develop models that predict molecular phenotypes, specifically gene expression and transcript abundance, directly from DNA sequences.

The field of molecular phenotype prediction is in the early stages of leveraging advances in machine learning. Recent advances in genomics and mass spectrometry now allow the generation of datasets of sufficient size and quality for training complex models (e.g. deep neural networks) on biological phenomena such as transcription and translation. However, few studies have been published in this domain and developing expertise in how best to approach this problem is a high priority for this project.

Project Description

In this project, we are leveraging comparative genomics and transcriptomics to identify differences in DNA sequence and gene expression between matched genes from closely related species (orthologs). Building on approaches and models from our previous project, we are introducing augmented datasets that include new species and multiple tissues to expand the accuracy and capabilities of these models. Specifically we are expanding our datasets to include multiple tissues from multiple pea varieties, lentils, chickpea and brassicas in addition to our original dataset that includes faba bean, barrel medic, grass pea and pea. This additional information will be integrated into the existing model and the architecture will be modified to accommodate multi-task prediction. The resulting models will be interrogated to interpret what is being learned by the models and augment our understanding of gene regulation. These models are expected to have wide ranging utility for crop improvement. By providing a means to evaluate trait associations and modeling 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.

Required Skills / Expertise

Were looking for a talented and enthusiastic Intern with a strong knowledge of computational biology and machine learning.

Key responsibilities:

• Build, train, and evaluate ML/DL models
• Undertake applied research on ML techniques to address the limitations in existing models and develop new approaches
• Collaborate with cross-functional teams to develop minimum viable products (MVPs) and client-centric solutions
• Engage in regular client meetings, contributing to presentations and reports on project progress
• Optimize ML pipelines to ensure efficiency, scalability, and real-time processing capabilities

Requirements:

• Completion of a graduate level program or higher (M.Sc/Ph.D) in Computing Science, ML, or a related field.
• Research and/or applied project experience in computational biology and related Deep Learning Technologies (e. g. Convolutional Neural Networks (CNN), Sequence models, Attention networks, Transfer learning)
• Proficient in Python programming language and related libraries and toolkits (e.g. scikit learn, Pandas, Jupyter notebooks, PyTorch, Keras, Tensorflow, transformers)
• A positive attitude towards learning and understanding a new applied domain
• Must be legally eligible to work in Canada

Assets / Nice to Haves:

• Experience working with data engineering workflows and databases
• Publication record in peer-reviewed academic conferences or relevant journals in machine intelligence
• Knowledge and experience in designing experimental frameworks for large datasets

Non-technical requirements:

• Interdisciplinary team player enthusiastic about working together to achieve excellence
• Capable of critical and independent thought
• Able to communicate technical concepts clearly and advise on the application of machine intelligence
• Intellectual curiosity and the desire to learn new things, techniques, and technologies

Why You Should Apply

Besides gaining industry experience, additional perks include:

• Work under the mentorship of an Amii Fellow and Amii Scientist for the duration of the project
• Participate in professional development activities
• Gain access to the Amii community and events
• Build your professional network
• The opportunity for a ongoing machine learning role at the clients organization at the end of the term (at the clients discretion)

About Amii

One of Canadas 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 worlds 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.

How to Apply

If this sounds like the opportunity you've been waiting for, please dont wait for the closing January 20, 2025 to apply - were excited to add a new member to the Amii team for this role, and the posting may come down sooner than the closing date if we find the right candidate before the posting closes! When sending your application, please send your resume and cover letter indicating why you think you'd be a fit for Amii. In your cover letter, please include one professional accomplishment you are most proud of and why.

Applicants must be legally eligible to work in Canada at the time of application.

Amii is an equal opportunity employer and values a diverse workforce. We encourage applications from all qualified individuals without regard to ethnicity, religion, gender identity, sexual orientation, age or disability. Accommodations for disability-related needs throughout the recruitment and selection process are available upon request. Any information provided by you for accommodations will be kept confidential and wont be used in the selection process.