Visiting Faculty Machine Learning Healthcare
Scale AI
Job Overview
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Job Description
About the Role
Scale AI is seeking a Visiting Faculty with expertise in Machine Learning applied to the Healthcare Domain. The ideal candidate will have a strong background in Computer Science, Bioinformatics, or a closely related field, and a proven track record of innovative research.
Responsibilities
The candidate will be responsible for developing a cutting edge research program. Key research areas include:
- Utilizing Electronic Health Records (EHRs) and non-clinical data for modeling health behaviors and outcomes.
- Implementing Natural Language Processing, Multi-modal AI, and Large Language Models for patient data analysis.
- Creating prediction models for clinical issues such as cancer and other disorders.
- Addressing bias and fairness in AI within healthcare and exploring deep genomics.
- Collaborating with Scale AI engineers on research and development projects.
Required Qualifications
A Ph.D. in Computer Science, Biostatistics, Biomedical Informatics, or a related quantitative field is required. The position demands demonstrated research excellence in Machine Learning and NLP applications in Healthcare, supported by high-impact publications or significant grant funding. Strong communication and collaboration skills are also essential.
Other Information
There is a 90-day waiting period before reconsidering candidates for the same role. Scale AI is committed to being an inclusive and equal opportunity workplace and adheres to all regulatory and privacy guidelines.
Key skills/competency
- Machine Learning
- Healthcare
- Research
- Natural Language Processing
- Data Modeling
- Collaboration
- Computer Science
- Bioinformatics
- Innovation
- Fairness
How to Get Hired at Scale AI
- Customize your resume: Highlight research in ML and healthcare.
- Showcase your publications: Emphasize high-impact research or grants.
- Engage on LinkedIn: Follow Scale AI and industry trends.
- Prepare case studies: Demonstrate practical ML applications in healthcare.
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