at University of Chicago in Chicago, Illinois, United States
Job Description
DepartmentBSD RAO - Operations
About the Department
The Tian Lab, within the Department of Radiation and Cellular Oncology, develops novel deep learning-based technologies with direct translational applications in radiotherapy.
The Department of Radiation and Cellular Oncology at The University of Chicago has been at the forefront of patient care, research, and education. The department is equipped with the most advanced technology and offers a variety of cutting-edge radiation therapies to patients and is always eager to discover more in the lab. This at-will position is wholly or partially funded by contractual grant funding which is renewed under provisions set by the grantor of the contract. Employment will be contingent upon the continued receipt of these grant funds and satisfactory job performance.
Job Summary
The Research Scientist I will be working on deep learning based translational research projects under the mentorship of Dr. Zhen Tian. This position will develop intelligent automatic treatment planning algorithms for a variety of radiotherapy treatment modalities using advanced deep learning technologies.
Responsibilities
- Develop a deep reinforcement learning model for automatic treatment planning for gamma knife radiosurgery.
- Develop a deep reinforcement learning model for automatic treatment planning for HDR brachytherapy.
- Develop a deep reinforcement learning model for automatic treatment planning for proton therapy.
- Participates in the preparation and writing of IRB protocol applications.
- Performs experiments and collects and analyzes data. Participates in the promotion of a research project.
- Participates in the preparation and writing of grant applications and reports, and co-authors/authors scientific research manuscripts. Provides peer review of grant applications and scientific manuscripts.
- Collaborates on development of new research projects with academic and community-based organizations.
- May present work at scientific meetings.
- Performs other related work as needed.
Minimum Qualifications
Education:
Minimum requirements include a PhD in related field.
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Work Experience:
Minimum requirements include knowledge and skills developed through ---
Certifications:
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Preferred Qualifications
Experience:
- Experience with deep learning.
- Experience with high-performance computing.
- Proficiency in python programming.
- Familiarity with TensorFlow or PyTorch.
Working Conditions
- Lab environment.
Application Documents
- Resume (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
FLSA Status
Exempt
Pay Frequency
Monthly
Scheduled Weekly Hours
40
Benefits Eligible
Yes
Drug Test Required
No
Health Screen Required
No
Motor Vehicle Record Inquiry Required
No
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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