“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. You’ll 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

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

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

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

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

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

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

“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. You’ll 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 Technolog