Postdoc: Health AI & Data Science Research Fellow

University of Minnesota; Saint Paul (MN); On-site; USD 54,907 - 67,108; Full time

Job summary

The University of Minnesota Department of Surgery, within the Division of Health Data Science, seeks two postdoctoral research associates to join multiple projects in health data science, artificial intelligence, natural language processing, and computer vision. These are annually renewable professional academic appointments.

Responsibilities include building risk prediction models, causal representation learning, and deep learning interpretation; conducting experiments; contributing to

Qualifications

• PhD in Biomedical/Health Informatics, CS, IS, NLP, Data Science, AI, or related field.
• Strong programming skills in Python, Java, etc.
• Expertise in machine learning, neural networks, and deep learning.
• Excellent writing and communication skills.
• Ability to function well in a fast-paced research environment and meet deadlines.

Responsibilities

• Develop risk predictive models for clinical outcomes.
• Conduct causal representation learning on real-world data.
• Apply deep learning methods with emphasis on interpretation, fairness and robustness.
• Run computational experiments across health and biomedical informatics projects.
• Assist with project development including literature reviews and study design.
• Deliver results and write/critique technical reports and publications.
• Communicate and present analytical results to researchers and faculty.

Skills

Python

Java

Machine learning

Deep learning

Writing

Fast-paced work

Education

PhD in Biomedical/Health Informatics, CS, IS, NLP, Data Science, AI or related

Job description

The University of Minnesota Department of Surgery, within the Division of Health Data Science, seeks two postdoctoral research associates to join multiple projects in health data science, artificial intelligence, natural language processing, and computer vision. These are annually renewable professional academic appointments.

Responsibilities include building risk prediction models, causal representation learning, and deep learning interpretation; conducting experiments; contributing to