The position will apply experience and interests in perceptual categorization, machine learning, statistical pattern recognition, and natural computation to a sizeable, diverse and growing collection of laboratory and web-derived ground truthing databases for color imaging. Join a team working on advanced optical feedback for commercial printing and provide knowledge of multi-variate clustering, to implement software to perform computational categorization.
Examples of resarch and technologies in the area of collective perceptual categorization from this lab include multi-lingual color naming experiments, deriving a family tree of print substrates, an online color thesaurus, color difference description and image quality evaluation.
A strong candidate will be a multi-disciplinary researcher with skills in one or more of the following areas: machine learning algorithms, perceptual categorization, statistics, image processing, human vision, computational linguistics, color science, psychometrics, software development and/or web-based IT. Candidates should have completed coursework in the areas of machine learning, intermediate statistics, multimedia databases and data mining and algorithms. Candidates should also have programming proficiency in one or more of the following: Matlab, C++, R or python.
Given the nature of the resarch, candidates will be considered from multiple technical backgrounds such as vision science, engineering, computer science and other fields. Sucessful candidates will know the difference between k-means and k-harmonic means, will be prepared to protoype ideas as software modules and will be comfortable in an advanced industrial resarch environment. The research will be conducted in collaboration with HP Labs principal scientist, Nathan Moroney.
Interested candidates should register at the ASEE/NSF sign-up page to be notified when the application process has been opened or email the listed contact.
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