Post-Doctoral Associate - Computational Health Sciences Division

at  University of Minnesota

Required Qualifications:

• A Ph.D. degree in Biomedical/Health Informatics, Computer Science, Information

Science, Computational Linguistics, Data Science, Artificial Intelligence, or a

related technical field.

• Strong programming skills in Python, Java, etc.
• Expertise in machine learning, neural networks, and deep learning
• Excellent writing and communication skills
• Highly motivated, ability to identify potential problems and develop solutions.
• Ability to function well in a fast-paced research environment, set priorities to

accomplish multiple tasks within deadlines, and adapt to ever changing needs

Preferred Qualifications:

• Research experience in natural language processing on biomedical and/or

clinical texts, computer vision on medical images, causal inference, predictive

modeling on longitudinal data.

• Expertise in transformers, reinforcement learning, transfer learning
• Experience with leading projects within a research team
• Working knowledge of modern web service and/or software implementation
• Ability to work collaboratively with a diverse group of research scientists with

Overview
Department of Surgery (DOS, https://med.umn.edu/surgery) at the University of Minnesota is seeking two postdoctoral research associates to join a team working on
multiple research projects in the field of health data science, artificial intelligence and natural language process, computer vision in the Division of Health Data Science (HDS)
at the DOS. The position is an annually renewable professional academic appointment.

Duties/Responsibilities:

• Risk predictive model for clinical outcomes
• casual representation learning for real-world data
• deep learning interpretation, fairness and robustness
• Regularly conduct computational experiments to execute algorithms on various

health and biomedical informatics projects

• Assist with new project development including literature review, study design.
• Work under general supervision but usually is given the latitude to make decision

on projects that s/he is accountable for delivering on; decisions may involve an
approach from among alternatives, determining how to best use available

resources

• Design and/or implement AI methods, summarize findings, and generate study

results for research topics

• Write, edit or critique technical reports and/or publications describing research

results to sponsors or funders of the research,

• Communicate and present analytical results to researchers, faculty, and other

academic professionals in associated fields of expertise (spoken presentations,
poster or written work summaries for display, panels, and other formats).
About the Program
Division of Health Data Science in DOS is a newly created division which focuses on development of novel data science methods to analyze biomedical big data for
advancing health care. The Natural Language Processing / Information Extraction (NLP/IE) Program (https://healthinformatics.umn.edu/natural-language-processing) at the Institute for Health informatics is a team of investigators, postdoctoral researchers, programmers, and students who work together on NLP for a variety of clinical and biomedical tasks.
About the Department
Committed to innovation and diversity, the Medical School educates physicians, scientists, and health professionals; generates knowledge and treatments; and cares for patients and communities with compassion and respect. We value excellence, inclusiveness, collaboration, and discovery.
To learn more about the Department of Surgery, please visit: https://med.umn.edu/surgery
Applications must be submitted online. To be considered for this position, please click the Apply button and follow the instructions. You will be given the opportunity to complete an online application for the position and attach a cover letter and resume.
Additional documents may be attached after application by accessing your “My Job Applications” page and uploading documents in the “My Cover Letters and Attachments” section.
To request an accommodation during the application process, please e-mail employ@umn.edu or call (612) 624-8647.
The University recognizes and values the importance of diversity and inclusion in enriching the employment experience of its employees and in supporting the academic mission. The University is committed to attracting and retaining employees with varying identities and backgrounds.
The University of Minnesota provides equal access to and opportunity in its programs, facilities, and employment without regard to race, color, creed, religion, national origin, gender, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http://diversity.umn.edu
Any offer of employment is contingent upon the successful completion of a background check. Our presumption is that prospective employees are eligible to work here. Criminal convictions do not automatically disqualify finalists from employment.
The University of Minnesota, Twin Cities (UMTC)
The University of Minnesota, Twin Cities (UMTC), is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
At the University of Minnesota, we are proud to be recognized by the Star Tribune as a Top Workplace for 2021, as well as by Forbes as Best Employers for Women and one of America’s Best Employers (2015, 2018, 2019, 2023), Best Employer for Diversity (2019, 2020), Best Employer for New Grads (2018, 2019), and Best Employer by State (2019, 2022)

• Risk predictive model for clinical outcomes
• casual representation learning for real-world data
• deep learning interpretation, fairness and robustness
• Regularly conduct computational experiments to execute algorithms on variou