Microscopy Intern

Elephas•Madison, WI

5d•Onsite

About The Position

Elephas is a Madison, WI based biotechnology start-up company working to build an instrument and assay platform that will inform how clinicians treat cancer patients around the world. We are seeking an Microscopy Intern to who is looking to join a group of motivated people driven to provide hope to cancer patients globally. We are looking for someone who is comfortable working in a fast-paced and highly collaborative environment. The ideal candidate will have a passion for scientific discovery and data-driven research, a commitment to team success, and be pursuing a degree in biomedical engineering, computer science, biology, bioinformatics, or a related field. This is a great opportunity to gain hands-on experience working with advanced biological imaging data, image processing techniques, and machine learning methods in a growing and innovative company.

Requirements

Currently working towards a degree in Biomedical Engineering, Computer Science, Bioinformatics, Data Science, Biology, or a related field
Experience with image processing or computer vision techniques
Familiarity with machine learning or deep learning frameworks
Experience working with large imaging datasets or multidimensional image data
Experience with data visualization and analysis tools
Self-starter, able to work independently and take initiative
Detail oriented with strong organizational skills

Responsibilities

Augment training datasets using image processing techniques such as flipping, rotating, scaling, and de-noising of microscopy images
Select, train, and optimize machine learning models for automated cell detection
Validate and test trained models to assess performance and support initial deployment
Work with high-density 3D biological imaging datasets
Collaborate with internal teams responsible for data acquisition, preprocessing, and generation of training labels
Coordinate with external consultants supporting image augmentation and advising on model selection
Perform manual identification and labeling of cells using training labels and tablet-based annotation tools
Identify cellular features using image contrast and structural characteristics such as nuclei and cell membranes
Participate in data analysis and contribute to development of automated detection workflows
Maintain organized documentation of datasets, model training, and experimental results