Team Scientists -- Translational Data Science #MED354

The University of Chicago
Chicago, IL, United States
21 days ago
Apply on www.jofdav.com
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
$79,868.0 - $162,020.0
Working hours
Regular working hours
Job source

Tech stack

Bioinformatics Health Informatics Dataspaces Machine Learning Cloud Platform System Information Technology

Job description

The University of Chicago’s Department of Medicine is searching for full-time faculty members on the School of Medicine track, at any rank, to join the Center for Translational Data Science, which focuses on the application of data science to research problems in biology, medicine and healthcare). We seek team scientists who will lead research focusing on: a) translational data science; b) the development of data commons, data ecosystem and other cloud computing platforms, systems and applications to support translational data science; and/or c) the development of machine learning, statistical, bioinformatics and AI algorithms to support translational data science. Other duties will include teaching and supervision of trainees and students, and scholarly activity. Academic rank and compensation are dependent upon qualifications.

Requirements

Prior to the start of employment, qualified applicants must have a doctoral degree or equivalent, in the following fields: Biomedical Data Science, Computer Science, Data Science, Informational Technology, Biomedical Informatics, or a related field.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.jofdav.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:36 min

Applying supervised machine learning for practical rule extraction

Katja Träumner

3:30 min

Approaching data problems with an engineering and strategy mindset

Becky Gandillon · LIVE

1:57 min

Evolution of machine learning algorithms and computing hardware

Alexandra Waldherr · LIVE

8:51 min

Addressing technical strategies and interdisciplinary computing dynamics

Noah Weber · LIVE

2:55 min

Transitioning from traditional software paths to machine learning engineering

Tarek Ziadé · Coffee With Developers

3:51 min

Overcoming hardware configuration barriers in machine learning

Jose Luis Latorre Millas · LIVE

Videos

See all

Related articles

See all