Pinnova · 5 hours ago
ManufacElectrical Engineering Intern, ML
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Responsibilities
Perform machine learning and deep learning on large-scale structured and unstructured datasets (images, free text) to extract biological insights.
Develop data pipelines, infrastructure, and computational tools for large-scale image analysis.
Provide scientific expertise and support for internal teams and external collaborators.
Conduct novel cancer genomics research using both public and internal datasets.
Prepare reports and presentations to communicate results in group meetings.
Present novel findings via abstracts or manuscripts.
Other duties as assigned.
Qualification
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Required
Bachelor’s Degree in Electrical Engineering, and 3+ years of work experience in relevant field; OR
Master’s Degree in Electrical Engineering, or other similar quantitative discipline and 2+ years of experience in relevant field
Preferred
Knowledge of cancer biology and cancer genomics
Experience with histopathology analysis
Strong experience with deep learning (particularly convolutional neural networks) methods and frameworks (Tensorflow, PyTorch, etc.) and a strong understanding of their mathematical foundations
Intermediate proficiency or higher in object-oriented programming with Python, Java, or C++
Experience with deep learning methods and frameworks (e.g. Tensorflow, PyTorch)
Experience with traditional machine learning methods and packages (e.g. sklearn) and a strong understanding of their mathematical foundations
Experience with distributed processing and computation (Spark, Horovod, job scheduling, etc.) for large-scale datasets
Familiarity with using cloud compute providers (AWS, GCP, etc.)
Previous authorship/co-authorship of relevant work
Strong communication and teamwork skills to work effectively in a flexible, cross-functional environment
Understanding of HIPAA and the importance of patient data privacy
Company
Pinnova
Pinnova is committed to helping individuals and organizations connect, grow, and achieve their potential.
Funding
Current Stage
Early StageCompany data provided by crunchbase