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AI/ML & Digital Health Internship

jd health
Found 1 month ago
Location
Remote, Switzerland
Duration (Months)
3 Months
Time
Full-time
Work Mode
Remote
Salary
Not disclosed
Visa Help
Not disclosed
Last Verified
1 month ago

Education

  • PhD

Skills & Qualifications

Technical Skills

  • AI
  • machine learning
  • Python
  • pytorch
  • predictive modeling
  • analysis of large datasets
  • sensor or wearable device data
  • semi-supervised and/or self-supervised learning
  • Foundation Models

Soft Skills

  • technical communication
  • presentation skills

Job Description

In this role, you will work with colleagues in the JNJ Innovative Medicine R&D Data Science & Digital Health organization and partner with key businesses across JNJ Innovative Medicine (formerly Janssen) to apply advanced data science and analytics to answer key research questions. As an intern, you will get the opportunity to work on cutting-edge problems at the intersection of AI and healthcare. You will be a member of a dynamic team comprising data scientists and subject matter experts to create and iterate on data science solutions. Projects include, but are not limited to, use of real-world data for modeling and prediction of disease severity and progression, discovery of disease subtypes, risk stratification, and precision medicine using traditional biostatistical methodologies and advanced methodologies from machine learning and/or deep learning. Interns could be located out of our US locations in San Diego, San Francisco, Titusville, Raritan, Spring House, Boston as well as remote.

Requirements

  • Currently pursuing a PhD degree in Statistics, Biostatistics, Data Science, Computer Science, Electrical Engineering, Computational Biology, Biomedical Informatics, or related quantitative discipline.
  • Strong working knowledge of AI and machine learning.
  • Proficiency in Python and pytorch.
  • Strong skills in predictive modeling and analysis of large datasets.
  • Strong technical communication and presentation skills.

Related Field

  • AI & Machine Learning

Related Subfield

  • Applied Machine Learning

Languages

  • English

Nice to Haves

  • Experience with processing and analyzing sensor or wearable device data.
  • Experience with semi-supervised and/or self-supervised learning.
  • Experience with Foundation Models
▶Apply Now

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