Machine Learning Resident (12 months)

at  Alberta Machine Intelligence Institute

“If you are interested in the application of machine learning to identify THC in infrared spectra, 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.”

• Taher Abbasi, Machine Learning Scientist

REQUIRED SKILLS / EXPERTISE

We’re looking for a talented and enthusiastic individual with solid knowledge of machine learning, demonstrated experience with supervised learning, and experience in applied settings.

REQUIRED QUALIFICATIONS:

• Completion of a graduate level program or higher (M.Sc/Ph.D) in Computing Science, ML or Engineering
• Research or project experience in machine learning and spectroscopy
• Proficient in Python programming language and related ML frameworks, libraries and toolkits (e.g. Scikit learn, Tensorflow, PyTorch, Pandas)
• A positive attitude towards learning and understanding a new applied domain
• Must be legally eligible to work in Canada

PREFERRED QUALIFICATIONS:

• Previous hands-on experience applying machine learning to spectroscopy
• Previous hands-on experience applying machine learning to real-world data
• Publication record in peer-reviewed academic conferences or relevant journals in machine learning

NON-TECHNICAL REQUIREMENTS:

• Desire to take ownership of a problem and demonstrated leadership skills
• 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 (note: at the discretion of the client). 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:

• Design, implement, and optimize ML algorithms specifically tailored for time-series applications
• Develop, train, and evaluate ML models customized for addressing challenges in time-series
• Prepare and curate time-series datasets for ML training and optimization
• Utilize relevant open-source tools to enhance data and ML tasks
• Conduct applied research on ML techniques, with a focus on overcoming obstacles within the time-series domain
• Collaborate with cross-functional teams and stakeholders 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