LCF- Guest Faculty Research Participant- Maria Pantoja
Job posting number: #7127941 (Ref:415322)
Posted: March 13, 2023
Application Deadline: Open Until Filled
Training and validation of Neural Networks (NN) are very computationally intensive. Deep Learning (DL) methods have dominated image processing lately and are gaining relevance in visual simulations. Most DL models assume that the input data distribution is identical between test and validation, but most often, they are not. In other cases, the test data is uncertain, and labeling may differ based on individual expert evaluations. This discrepancy makes DL less reliable for tasks like traffic signal recognition for self-driving cars, medical images where labels are difficult to assign, and other tasks where the precision of the output is crucial. By adding the capability of propagating uncertainty to our results, image processing models can provide not just a single prediction of the identification but a distribution over predictions.
We would like to explore the differences in uncertainty evaluations using three different methods: Ensembles; Multiple Input Multiple Outputs; and Peer Loss Functions
Job FamilyVisiting Faculty Appointment
Job ProfileGuest Faculty Research Participant
Worker TypeShort-Term (Fixed Term)
Time TypeFull time
As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.
All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.
Please note that all Argonne employees are required to be vaccinated against COVID-19. All successful applicants will be required to provide their COVID-19 vaccination verification as a condition of employment, subject to limited legally recognized exemptions to COVID-19 vaccination.
Argonne is an equal opportunity employer, and we value diversity in our workforce. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne prohibits discrimination or harassment based on an individual's age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.