Research Associate I - College of Engineering - Biomedical Engineering

Carnegie Mellon University•Pittsburgh, PA

11d•Onsite

About The Position

Carnegie Mellon University's Biomedical Engineering (BME) Department, within the College of Engineering, is renowned for its interdisciplinary approach to solving complex healthcare challenges. The Engineered Morphogenesis Group is seeking a Laboratory Automation Engineer (Part-Time / Intern, 20 hours/week anticipated) to help devise, trial, and test new automated workflows in CMU’s Biological & Chemical Innovation Cloud Lab (Cloud Lab) to support increased throughput in cell and organoid culture. This role combines robotics and tissue engineering to create a robust and reproducible culture platform for high-volume testing of environmental pollution exposure, viral and bacterial infection, and therapeutic screening. This role is ideal for engineers who enjoy problem-solving, hands-on experimentation, have strong attention to detail, and value developing technologies with tangible real-world applications.

Requirements

Currently pursuing or recently completed a MS degree in Computer Science, Biomedical Engineering, Mechanical Engineering, or a related field. Outstanding recent BS graduates will also be considered.
Proficiency in constructing machine learning models for describing and predicting experimental outcomes.
Ability to work onsite in Pittsburgh, PA.
A combination of education and relevant experience from which comparable knowledge is demonstrated may be considered.
Successful Background check

Nice To Haves

Prior experience in laboratory automation for life sciences is highly desirable.
Prior experience with cell and tissue culture is desirable but not required.

Responsibilities

Learn the fundamentals of cell and organoid culture, achieving proficiency as monitored by supervisors.
Become familiar with existing experimental workflows.
Learn and use automation technologies available in the Cloud Lab, including both hardware and software systems.
Integrate automation technologies into existing workflows with an emphasis on throughput and reproducibility.
Synthesize and analyze data generated from automated experiments to inform iterative workflow improvement.
Work closely with cell culture teams to develop and integrate automated workflows.
Regularly participate in and present during group meetings, covering project overviews, results, and future goals.