Miami University

Postdoctoral Associate - Hussman Institute

Miami, FL, USA · Staff+ · Contract

Sponsorship not specifiedDetected 15 days ago
PythonMachine LearningNLPLLMsStatisticsHRISPatient CareResearchCommunication

About the role

  • Conducts research on specific areas of study as assigned.
  • Prepares research reports and technical papers for publishing.
  • Investigates the feasibility of applying a wide variety of scientific principles and theories.

Responsibilities

  • Design and execute end-to-end statistical and bioinformatic analyses integrating large-scale genomic and clinical datasets to identify genetic contributors to disease risk within the All of Us Research Program secure Researcher Workbench environment using R and/or Python.
  • Develops advanced analytical models and systems and provides solutions and analyses to support strategic and tactical decisions.

Requirements

  • Doctorate degree in relevant field required
  • No previous experience required
  • Ability to learn new procedures, technologies, and protocols, and adapt to changing priorities and work demands.
  • Ability to work collaboratively with others and contribute to a team environment.
  • Teamwork: Ability to work collaboratively with others and contribute to a team environment.
  • Technical Proficiency: Skilled in using office software, technology, and relevant computer applications.
  • Proficiency in large language model (LLM) and natural language processing methods for analysis of unstructured clinical and biomedical data.
  • Experience with cloud-based and high-performance computing environments to support scalable data processing and model development.

Skills

  • Knowledge, Skills and Abilities:
  • Communication: Strong and clear written and verbal communication skills for interacting with colleagues and stakeholders.

Benefits

  • Strong proficiency in machine learning and statistical modeling, including development and validation of predictive models using large-scale data.
  • Proficiency in Python and/or R for statistical analysis, machine learning, and biomedical data processing.
  • Develop and apply machine learning and large language model (LLM) approaches to build interpretable, clinically relevant predictive tools for disease diagnosis and risk stratification.
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