Edtech.com's Summary

The Pennsylvania State University is hiring Part-Time Programmer Analysts to support the NIH National Center for Advancing Translational Science-funded project, The Biomedical Data Translator. The role involves developing components for the ARAX system, including implementing machine learning techniques, creating python wrappers for API integration, automating filtering processes, and maintaining the code base while collaborating remotely with a team.

Highlights

Develop machine learning and statistical methods to enhance graph edge properties in ARAX.
Create modular Python wrappers interfacing with NCATS translator API endpoints and UI.
Implement automated thresholding and a tunable weighting scheme for scoring ARAX results.
Train graph node embedding and random forest models on clinical and biomedical data for drug-disease prediction.
Maintain ARAX codebase compliance with evolving API and data standards.
Experience required with Neo4j graph database, REST APIs, Python (3+ years), machine learning packages like Scikit-learn, Git, and GitHub (3+ years).
Master's degree with two years related experience or equivalent combination of education and experience.
Ability to work effectively remotely with large, interdisciplinary teams.
At least two years working with biomedical ontologies, datasets, and publicly available databases.
Compensation and work hours are negotiable within funder restrictions.

Part-Time Programmer Analysts Full Description

APPLICATION INSTRUCTIONS:

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Approval of remote and hybrid work is not guaranteed regardless of work location. For additional information on remote work at Penn State, see Notice to Out of State Applicants.

JOB DESCRIPTION AND POSITION REQUIREMENTS

The Department of Computer Science and Engineering is seeking part-time Programmer Analysts to support an NIH National Center for Advancing Translational Science-funded project called: "The Biomedical Data Translator." https://ncats.nih.gov/research/research-activities/translator

These programmers will develop the following components to be incorporated into the ARAX (https://github.com/RTXteam/RTX) system: Implement statistical and machine learning techniques to augment graph edge properties in ARAX; Create modular python wrappers to interface with NCATS (National Center for Advancing Translational Services) translator API (Application Programming Interface) endpoints (https://smart-api.info/registry) and UI (https://ui.transltr.io/) to allow ingestion of open access data into (and from) the ARAX system; Implement automated thresholding of ARAX filtering components; Create a modular, tunable weighting scheme for scoring and ranking of results to be returned by the ARAX system; Train a graph node embedding + random forest model on open access clinical and biomedical data to extend current capabilities for predicting the probability that a drug treats a disease; Maintain the ARAX code base to retain compliance with API and data standardization version updates as specified by the advisory, API, and data modeling committees; Address additional ARAX code-base issues as they are identified and assigned by faculty member.

Requirements include:

Excellent interpersonal and communication skills
Strong attention to detail
Master's degree plus two years of related experience, or an equivalent combination of education and experience
Specific skills required are experience with the Neo4j graph database, interacting with REST APIs, more than three years' experience with Python, and associated machine learning packages (such as Scikit learn)
More than three years' experience with Git and GitHub
Ability to effectively work remotely with a large team of programmers and scientists
Two years' or more experience working with biomedical ontologies/data sets and publicly available databases

Pay and hours per week negotiable within funder restrictions. Please include a cover letter and resume.

BACKGROUND CHECKS/CLEARANCES
Employment with the University will require successful completion of background check(s) in accordance with University policies.

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