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Open nowPosted 3 days ago

Postdoctoral Fellow (Data Engineering & AI/ML)

The Geneva Foundation33 open roles

Pay
$65,000 – $79,000 a year
Where
Bethesda, MD
Work mode
On site
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Your applicationOpen nowPostdoctoral Fellow (Data Engineering & AI/ML)The Geneva Foundation · Bethesda, MD

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The clock on this job

Early applications get read.

13.4% of postings close within 7 days. Measured by our own scanner across the market. The Geneva Foundation postings stay open a median of 61 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.6%3 days
  3. 13.4%7 days
  4. 21.6%14 days
  5. 36.2%30 days
  6. 59.0%60 days
This job: posted 3 days ago

The Geneva Foundation median: 61 days open

The posting

About the Position

The Post-Doctoral Fellow I provides assistance to the Principal Investigator, Co Investigators, and Research Staff. S/he ensures enrollment numbers are successfully met, when applicable, and protocol adherence, monitoring visits, overseeing regulatory and administrative details, data management assistance and project close-out support is provided.

About the Project

This project is a multi-institution research collaboration with Stanford, Johns Hopkins, the Veteran Affairs (VA), and the Department of War (DoW) to build a sleep foundation model. Unlike traditional AI models, a sleep foundation model relies on self-supervised learning on vast amounts of physiological sleep study data (petabyte-level). By generating representations (embeddings) from the sleep data, the team aims to forecast future health risks and uncover latent patterns that traditional sleep scoring metrics miss.

Salary Range

$65,000 - $79,000. Salaries are determined based on several factors including external market data, internal equity, and the candidate’s related knowledge, skills, and abilities for the position.

Qualifications

  • PhD or MD required
  • 1-2 years medical research project management experience strongly desired, preferably with Department of Defense
  • 5+ years experience with multimodal data analysis
  • Proven experience with signal processing (eg, time-series biosensor data)
  • Hands-on experience with version control systems (eg, Git) and ability to work in a collaborative coding environment
  • Experience and/or a strong desire to learn the ins and outs of cloud computing, including managing cloud-native data warehouses and standing up ETL pipelines
  • Preferred qualifications: strong knowledge of sleep science, familiarity with machine learning workflows including model development, model tuning, and deploying models at scale
  • Ability to work in a fast-paced, multidisciplinary, multi-site (sometimes asynchronous) team environment
  • Knowledge of military system, preferred.
  • Demonstrate competence in oral and written communication
  • Must be organized, attentive to detail, and possess a positive, friendly and professional demeanor
  • Ability to be flexible with changing priorities and available to interact with employees, clients and sponsors of all levels
  • Ability to exercise independent judgment in fast-paced, high-pressured environment with strict deadlines and to manage multiple projects
  • Computer skills including at least intermediate level experience with Excel and other Microsoft Office Software

Responsibilities

  • Design, develop, and deploy scalable applications (both front-end interfaces and back-end data pipelines) to support large-scale research
  • Build ETL pipelines that ingest multimodal datasets (wearables, cross-referenced with EHRs, PSGs, and subjective sleep metrics), ensuring efficient storage, retrieval, and processing of massive datasets (+1million records)
  • Support the build-out of scalable infrastructure to support advanced machine learning models using physiological features and sleep microarchitectures
  • Optimize application performance and scalability through performance tuning, code refactoring, and database optimization techniques
  • Stay updated with emerging industry trends, academic literature, and technologies in data engineering, cloud architecture, and machine learning to continuously improve the lab’s technical capabilities
  • Ensure data integrity throughout engineering workflows. Assist in the preparation of Standard Operating Procedures (SOPs), analytical frameworks, and technical documentation
  • Maintain open communication with leadership, advise on technical processes, and curate progress reports
  • Provide technical support and oversight to team members with less experience
  • Assist in regulatory support, Data Sharing Agreements, and other project documentation
  • Collaborate and assist the Principle Investigator, Co-Investigators and research team in the performance the research study
  • Contribute to the drafting and submitting of abstracts, manuscripts and reports for dissemination
  • Prepare progress reports on research as required
  • Analyze and write-up data
  • Present complex information effectively to a range of audiences
  • Provide data management assistance
  • Provide project close-out suppo
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