Astrophysics Research Associate 6 in Data Science for Machine Learning and Citizen Science

Submission Information
Publish Date: 
Saturday, November 21, 2020
Archive Date: 
Saturday, December 19, 2020
To event remaining 13 days
Job Summary
Job Category: 
Post-doctoral Positions and Fellowships
Institution Classification/Type: 
Large Academic
Institution/Company: 
University of Minnesota
Department Name: 
School of Physics and Astronomy
Street Line 1: 
John T. Tate Hall, 116 Church Street SE
City: 
Minneapolis
State/Province: 
MN
Zip/Postal: 
55455
Country: 
United States of America
Announcement
Job Announcement Text: 

The Zooniverse team under the guidance of Professor Lucy Fortson in the School of Physics and Astronomy at the University of Minnesota has openings for three Research Associate positions; two focused on machine learning and data science applications in Astrophysics; one focused on machine learning and data science applications in Medical Imaging and Humanities. This job description is for the lead Astrophysics position. We seek a research associate with strong experience in data science and machine learning to further develop the world’s largest platform for citizen science. Zooniverse uses the combined input from over 2 million volunteer classifiers to provide labeling and other tasks across hundreds of research projects in a variety of domains.  Under the onslaught of ever-larger amounts of data and to take advantage of improvements in machine algorithms, the Zooniverse team has built and implemented infrastructure to enable novel approaches that optimally combine human and machine classifiers. We seek individuals who are excited by the idea of using this new infrastructure on data-intensive sub-fields of Astrophysics with the goal of most efficiently classifying “known-knowns” while at the same time enabling the serendipitous discovery of completely new classes of objects in a given astrophysical dataset. For a full description please see here: https://hr.myu.umn.edu/jobs/ext/338067 <https://hr.myu.umn.edu/jobs/ext/338067>

Qualifications:
Required: Applicant must hold a Ph.D. in a relevant subject (e.g. in a data-intensive field such as Physics or Astronomy) or in computer science. It is essential that the applicant have demonstrated experience with a set of tools appropriate for working with large-scale data science including application of machine learning.  A strong publication record in relevant academic field(s) is also required as is the ability to mentor students and work in a diverse, distributed team in an interdisciplinary manner with an ability to direct one’s own research.

Preferred: Preference will be given to applicants who have experience implementing machine-learning algorithms in a research context in either academia or industry as well as demonstrated familiarity with classifier combination problems or with research into human-computer systems; a demonstrated interest in citizen science; the ability to manage multiple projects; experience in managing working groups or small teams; excellent organizational, presentation and writing skills; and demonstrated self-motivation and creativity. While based in Minneapolis, the successful applicant will be expected to travel to Chicago and Oxford, UK.

 

Pay Compensation
Salary Min: 
$49805.00 (USD)
Salary Max: 
$57000.00 (USD)
Included Benefits: 
Application Deadline: 
Friday, January 8, 2021
Selection Deadline: 
Monday, February 15, 2021
Current Status of Position: 
Accepting Applicants
Apply to Job
Attention To: 
Lucy Fortson
Title: 
Professor
Institution/Company: 
University of Minnesota
Department Name: 
School of Physics and Astronomy
Street Line 1: 
John T. Tate Hall, 116 Church Street SE
City: 
Minneapolis
State/Province: 
MN
Zip/Postal Code: 
55455
Country: 
United States of America
Email: 
lfortson@umn.edu
Institution/Company Job ID or Reference Code: 
338067
Inquiries About Job
Attention To: 
Professor Lucy Fortson
Subject: 
Astrophysics Research Associate 6 in Data Science for Machine Learning and Citizen Science
Email: 
lfortson@umn.edu