Machine Learning Internship at Adobe Research, San Jose, CA
The Machine Learning Group at Adobe Research in San Jose is looking for interns to work on a range of problems in machine learning, big data, digital marketing, and analytics. Our interns will have the opportunity to work on real-world terabyte-scale problems in Adobe Marketing Cloud (http://www.adobe.com/marketing-cloud.html). The interns will be supervised by researchers in the group who have excellent publication record with dozens of papers at top-tier machine learning and AI conferences and journals in recent years. The research topics of interest include: Machine Learning topics - Large-scale reinforcement learning, online learning, bandits (contextual and combinatorial), A/B and hypothesis testing - Graphical model and approximate inference - Deep learning and representation learning - Time-series prediction and spatial-temporal analysis - Causal inference - Risk analysis & risk-sensitive optimization - Anomaly and change detection in high-dimensional data Applications - Data cleansing - imbalanced data, categorical variables, missing values, dimensionality reduction, feature selection - Lifetime value & churn prediction - Large-scale recommender systems - Attribution modeling - Bid optimization in online advertising - Activity recognition (from web, mobile and location data) - Big data visualization The interns will be affiliated with the Big-data Experience Lab (BEL) at Adobe Research (http://www.adobe.com/technology.html) located in San Jose, California, at the heart of the Silicon Valley. The duration of the internship is 12 weeks and it can start any time from April 1, 2016. Beyond Adobe's traditional strength in media technologies, the BEL lab is focusing on areas related to digital marketing and analytics, in particular problems related to Adobe's Digital Marketing Cloud. Adobe is one of the biggest providers of digital marketing and analytics solutions with customers including big banks, hotels, online retails, insurance and entertainment companies. The successful candidate will be mentored and work closely with one or more of the following Adobe researchers: - Hung Bui (https://sites.google.com/site/buihhung) - Mohammad Ghavamzadeh (http://chercheurs.lille.inria.fr/~ghavamza) - Jaya Kawale (http://www-users.cs.umn.edu/~kawale) - Branislav Kveton (http://www.bkveton.com<http://www.bkveton.com/>) - Georgios Theocharous (http://www.adobe.com/technology/people/san-jose/georgios-theocharous.html) - Nikos Vlassis (https://sites.google.com/site/<https://site>nikosvlassis) Requirements: The applicants should be either at the final stage of a Master's or in a Ph.D. program in Computer Science, Statistics, Operations Research, Applied Mathematics or related fields, with a strong background in machine learning and good programming skills. We are particularly interested in candidates with prior exposure to optimization, statistics, reinforcement learning, bandit algorithms, and scalable machine learning. Application Submission: The deadline for the application is January 31, 2016, but we encourage the applicants to apply as soon as possible. The screening of the candidates will start on February 1, 2016, and will continue until the positions are filled. The application should include a brief description of the applicant's research interests and past experience, plus a CV that contains the degrees, GPAs, relevant publications, name and contact information of up to two references, and other relevant documents. To apply, please send your application to <ghavamza(at)adobe(dot)com>.
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