Computational Scientist - Artificial Intelligence & Machine Learning at The University of Chicago in Chicago, Illinois

Job Description:

Department

Provost Research Computing Center



About the Department

The University of Chicago Research Computing Center (RCC), a unit in the Office of Research, provides high-end research computing resources to researchers at the University of Chicago. It is dedicated to enabling research by providing access to centrally managed High Performance Computing (HPC), storage, and visualization resources. These resources include hardware, software, high-level scientific and technical user support, and the education and training required to help researchers make full use of modern HPC technology and local and national supercomputing resources. The Office of Research oversees the conduct of sponsored research, research program development, and contract management functions.



Job Summary

The job develops software to support the data acquisition, ingestion, and integration for research projects. Assists in the development of user interfaces and scalable back-end services to automate and accelerate the scientific output of multi-institutional research projects.


The Research Computing Center (RCC) seeks to hire an experienced Computational Scientist - Scientific-AI and Machine Learning to serve as a domain expert in supporting and advising faculty, post-docs, and graduate students on projects in a wide range of research domains. In this role, the Computational Scientist will support research projects that needs to use machine learning and AI, understand faculty's research questions and contribute to finding solutions and developing applications. Working as part of an existing team, the successful candidate will have ample opportunity to contribute to enabling science at UChicago, to collaborate on software development, develop and deliver training, and other activities designed to advance research through scientific visualization, machine learning, and beyond.


This is a hybrid position requiring at least 3 days of onsite work.



Responsibilities

• Support applications of Artificial Intelligence (AI) in various research disciplines and serve as the domain expert.
  
  


• Work closely with faculty to identify, develop, and implement useful computational methods and resources that support or advance their research. Independently and proactively propose and execute practical solutions to research challenges.
  
  


• Develop and implement AI and machine-learning based methods for different use cases: images, video, speech, unstructured text, etc.
  
  


• Develop, maintain, and support data analysis, AI and Machine Learning pipelines.
  
  


• Confidently solve regression, classification, clustering, forecasting, and anomaly detection problems using established machine learning techniques.
  
  


• Independently propose and execute practical solutions to various research challenges.
  
  


• Communicate highly technical information to numerous audiences, including faculty, students, researchers, and staff. Teach others and learn new techniques.
  
  


• Help faculty with grant proposals by contributing sections describing the interplay between research objectives and new or expanded data resources.
  
  


• Create and present tutorials, hands-on workshops, and documentation to train the research community.
  
  


• Develops and presents technical training materials and web-based documentation. Ensures timely systems support and updates. Assists in conducting information security assessments and risk analysis of computing environment.
  
  


• Evaluates past and present technologies to help develop new tools. Ensures all the new tools have been through quality control reviews.
  
  


• Performs other related work as needed.
  
  

Minimum Qualifications




Education:

Minimum requirements include a college or university degree in related field.



Work Experience:

Minimum requirements include knowledge and skills developed through 2-5 years of work experience in a related job discipline.



Certifications:




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Preferred Qualifications




Education:

• Ph.D. in computer science, computer engineering, data science, or similar.
  
  

Technical Skills or Knowledge:

• Experience with one or more machine learning and deep learning frameworks such as TensorFlow, PyTorch, or Keras.
  
  


• Experience applying latest AI/ML techniques in computer vision and image classification analysis.
  
  


• Experience with one of more following AI/ML domains: Causal AI, Reinforcement Learning, Generative AI, NLP, Dimension Reduction, Computer Vision, Sequential Models.
  
  


• Experience using AI/ML techniques to solve real-world applications.
  
  


• Proficiency in Python.
  
  


• Experience with one or more Python libraries such as NumPy, Pandas, SciPy, Scikit-Learn, MatplotLib, Seaborn, geopy, NLTK.
  
  


• Experience with one or more high-level programming languages such as C/C++, Matlab, or R.
  
  


• Experience with Git and in general with version control.
  
  


• Experience with Linux/Unix.
  
  


• Experience with statistical and numerical methods.
  
  

Preferred Competencies

• Excellent interpersonal, verbal, written, and presentation skills.
  
  


• A broad knowledge of algorithms, programming languages, and libraries.
  
  


• Ability to understand and translate researchers' scientific goals into computational requirements.
  
  


• Ability to identify and gain expertise in appropriate new technologies and/or software tools.
  
  


• Ability to function as part of an interactive team while demonstrating self-initiative to achieve project's goals and Research Computing Center's mission.
  
  


• Demonstrated ability to work collaboratively within cross-functional teams.
  
  


• Strong analytical skills and problem-solving ability.
  
  


• Ability to work well with faculty and researchers.
  
  


• Experience writing or contributing to grant proposals.
  
  

Application Documents

• Resume or CV (required)
  
  


• Cover letter (preferred)
  
  

When applying, the document(s) MUSTbe uploaded via the My Experience page, in the section titled Application Documents of the application.



Job Family

Research



Role Impact

Individual Contributor



Scheduled
Weekly Hours

37.5



Drug Test Required



No



Health Screen Required



No



Motor Vehicle Record Inquiry Required



No



Pay Rate Type

Salary



FLSA Status

Exempt



Pay Range

$80,750.00 - $104,500.00
The included pay rate or range represents the University's good faith estimate of the possible compensation offer for this role at the time of posting.



Benefits Eligible

Yes
The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the Benefits Guidebook.



Posting Statement



The University of Chicago is an Affirmative Action/Equal Opportunity/Disabled/Veterans and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, gender identity, national or ethnic origin, age, status as an individual with a disability, military or veteran status, genetic information, or other protected classes under the law. For additional information please see the University's Notice of Nondiscrimination.


Staff Job seekers in need of a reasonable accommodation to complete the application process should call 773-702-5800 or submit a request via Applicant Inquiry Form.


We seek a diverse pool of applicants who wish to join an academic community that places the highest value on rigorous inquiry and encourages a diversity of perspectives, experiences, groups of individuals, and ideas to inform and stimulate intellectual challenge, engagement, and exchange.


All offers of employment are contingent upon a background check that includes a review of conviction history. A conviction does not automatically preclude University employment. Rather, the University considers conviction information on a case-by-case basis and assesses the nature of the offense, the circumstances surrounding it, the proximity in time of the conviction, and its relevance to the position.


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