Senior Research Scientist; PREP

PREP Research Associate

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields.

Individuals in this position will perform technical work supporting the collaboration's scientific research.

Research

Title:

Reference Material creation to Calibrate AI Solutions across labs

ITL's role in the IMS project "Distributed Manufacturing of First-In-Class NIST Traceable Active Cell Reference Materials" involves the following tasks: (1) the design and training of convolutional neural networks (CNN) for cell segmentation, cell division detection across time, and label-free cell viability assessment under different imaging modalities, and (2) the design of reference materials with which to transfer AI models across labs.

Our success depends upon the availability of highly skilled domain experts. We are challenged with difficult tasks that require not only expertise in running different types of CNNs, but also in designing new architectures for applications where training data is scarce but high accuracy is paramount.

U.S. Citizen Preferred

• Developing new AI architectures for meniscus removal using image-to-image regression networks.
• Create a solution that can generalize the meniscus removal regardless of the content being imaged.
• Create a model that predicts an image quality metric based on a group of existing blur metrics from the literature and compare that solution with a regular convolutional neural network.
• Produce high-quality publications based on research and results presented at internal and external meetings and conferences.
• Carrying out statistical analyses and interpretation of results.
• Compiling and cleaning large datasets.
• Determining methods to analyze complex data for validation.
• Developing innovative methods and research directions that support goals.
• Co-authoring reports on results of analyses.
• Presenting results at internal meetings and occasional meetings with external stakeholders.
• Ensuring that results, protocols, datasets, and documentation have been archived or otherwise transmitted to the larger organization.

• US citizenship is preferred.
• A PhD degree in Computer Science with 3 or more years of relevant experience.
• Expertise in PyTorch/Python and state of the Art AI models like vision transformers and advanced CNNs.
• Ability to build deployable complex software solutions for cell image analysis.
• Strong oral and written communication skills and strong presentation skills.

The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. To that end, the university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristics. The university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors or demographic characteristics that are irrelevant to the program involved.

The referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.

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