Machine Learning Research Intern at D-Wave Systems (Burnaby, BC, Canada)

Machine Learning Research Intern (Bioinformatics)
Location: Burnaby, BC

About us:
D-Wave is looking for exceptionally motivated people who love to see the impact of their work on a daily basis, who are driven to ensure the success of the company, and who want to be a part of something special.  We are working to change what is possible with computers and are leading the effort to apply quantum hardware to accelerate machine learning. 

Conditions such as autism, heart disease, and cancer have diverse biochemical manifestations and substantial genetic liability.  We can better understand the nature of these diseases, and identify new targets for treatment, by characterizing the changes in gene transcription that underlie them.  Traditional statistical approaches to analyzing the transcriptome are inherently linear, including those used to identify differential gene expression, extract modules of correlated genes, and remove confounding sources of variation. However, biological processes including gene regulation often exhibit complex, nonlinear dynamics.  We are therefore working to apply state-of-the-art nonlinear machine learning techniques to the analysis of gene expression data.

To assist is in this effort, we are seeking interns with familiarity in genetics and experience with modern machine learning methods, ideally generative models.

Position Description:

In this role, you’ll work with our team of machine learning experts to advance statistical analysis in genomics. You’ll work on novel machine learning algorithms that that push the state of the art in machine learning and which provide improved predictive insight of gene expression data. 
For the projects that we currently have in mind, we are only currently looking for interns who are available for 8 - 12 months. This is a paid position. 

Responsibilities Include:

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• Working closely with machine learning experts to design, implement, tune, and optimize machine learning algorithms
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• Implementing experiments to test the quality of machine learning algorithms
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• Validating promising research concepts with high quality Python implementations
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• Applying software engineering best practices: software design patterns, version control, unit tests, documentation
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Required Qualifications:

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• Enrolled in a PhD program in computer science, statistics, applied mathematics, bioinformatics, biology or a related field
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• Experience in modern machine learning methods, ideally generative models
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• Experience with deep learning frameworks such as TensorFlow or Pytorch would be an asset. 
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• Strong coding abilities
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• Self-motivated, proactive and flexible with excellent problem-solving skills
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