Biomedical ML & Imaging Systems Co-op

MIT Lincoln Laboratory; Lexington (MA); On-site; USD 32,000 - 44,000; Full time

Benefits offered by this job

Technical Co-Op: $25 – $32 per hour

Administrative Co-Op: $23 – $27 per hr

Job summary

MIT Lincoln Laboratory is seeking aHuman Health and Performance Systems Co-op for Spring 2027 in Lexington, MA. You will work with engineers, scientists, and clinicians to tackle AI/ML and image processing challenges in biomedical technologies, building workflows for large biomedical datasets and evaluating algorithms that advance medical imaging.

You will gain hands-on experience in a multidisciplinary setting, applying high-performance computing and Python-based tools to real-world patient

Qualifications

• Candidate is enrolled in a relevant BS/MS/PhD program.
• Strong theoretical knowledge of AI/ML methods and model architectures.
• Hands-on AI/ML development experience with Python and PyTorch.
• Experience in biomedical signal or image processing.

Responsibilities

• Collaborate within a multi-disciplinary team of engineers, scientists, and clinicians.
• Prepare and process large multimodal biomedical datasets (ultrasound, x-ray, microscopy).
• Develop and evaluate machine learning algorithms for biomedical imaging.
• Create data visualization tools for advanced medical imaging applications.

Skills

AI/ML proficiency

Python

PyTorch

Biomedical signal/image processing

Education

B.S./M.S./Ph.D. student in Biomedical Eng, Electrical Eng, CS or related field

Tools

Python

PyTorch

MATLAB

Job description

MIT Lincoln Laboratory is seeking aHuman Health and Performance Systems Co-op for Spring 2027 in Lexington, MA. You will work with engineers, scientists, and clinicians to tackle AI/ML and image processing challenges in biomedical technologies, building workflows for large biomedical datasets and evaluating algorithms that advance medical imaging.

You will gain hands-on experience in a multidisciplinary setting, applying high-performance computing and Python-based tools to real-world patient