Internship: Uncertainty Estimation for Deep Network Predictions

Cambridge, Massachusetts, United States · CV · 1304


While deep networks have been highly successful at visual regression problems such as face landmark estimation and skeleton tracking, relatively little work has been done on estimating their prediction error. We are seeking a highly motivated intern to conduct original research in the area of uncertainty estimation for deep network predictions. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The ideal candidate would be a senior PhD student in computer vision and machine learning with a strong publication record and experience in deep learning-based facial or body landmark estimation and tracking. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch and TensorFlow, and broad knowledge of machine learning and deep learning methods are expected.

Research Areas: Artificial Intelligence, Computer Vision, Machine Learning

Contact: Tim Marks

Mitsubishi Electric Research Labs, Inc. "MERL" provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, MERL complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

MERL expressly prohibits any form of workplace harassment based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. Improper interference with the ability of MERL’s employees to perform their job duties may result in discipline up to and including discharge.

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