Microsoft Research NYC seeks outstanding applicants for full-time researcher positions in the area of machine learning or a related field, such as statistics, computer vision or natural language processing. We invite applicants at all levels.
Deadline for full consideration: January 1, 2016. Application instructions are at the end of this e-mail. For additional postdoc and researcher positions, see: http://research.microsoft.com/en-us/labs/newyork Microsoft Research offers an exhilarating and thriving environment for cutting-edge, multidisciplinary research, both theoretical and applied, with access to an extraordinary diversity of big and small data sources, an open publications policy, and close links to top academic institutions around the world. We seek applicants from all areas of machine learning and related fields with a passion and demonstrated ability for independent research, including a strong publication record at top research venues. We especially welcome candidates who will complement our efforts to expand the scope and effectiveness of machine learning approaches to both new and existing domains. Researchers in our lab define their own research agenda, driving forward an effective program of basic and applied research. In addition to working on challenging and fundamental problems, they have the potential to realize their ideas in products and services used worldwide. Microsoft Research New York City is the newest MSR lab, comprising thirty full-time researchers and postdocs working in machine learning, systems, computational social science, algorithmic economics, information retrieval, and social media. The lab is highly collaborative and interdisciplinary, and is actively engaged with the local academic and tech communities. Examples of current machine learning projects include: * Active learning: When labeled data is scarce and unlabeled data abundant, how can machine-learning algorithms adaptively request labels to attain good generalization? (Alekh Agarwal, Tzu-Kuo Huang, John Langford, Rob Schapire) * Bayesian latent variable modeling: How can we use and develop Bayesian latent variable models (including statistical topic models and nonparametric Bayesian models) to answer exploratory, explanatory, and predictive questions about the structure, content, and dynamics of social processes? (Hanna Wallach) * Contextual bandits, exploration and incentives: What are the theoretical foundations and practical algorithms for learning over the course of interaction with a user in the presence of contextual information? How can algorithms learn over time from partial information provided by self-interested agents? (Alekh Agarwal, Miro Dudik, John Langford, Rob Schapire, Alex Slivkins, Vasilis Syrgkanis, Jenn Wortman Vaughan) * Ethics of Machine Learning: How can we study issues of fairness, accountability, and transparency in machine learning? (Kate Crawford, Fernando Diaz, Hanna Wallach) * Game theory and machine learning: How can machine-learning methods be used in settings involving interaction between players? And how can ideas from game theory be used in the design of machine-learning algorithms? (Alekh Agarwal, Miro Dudik, Rob Schapire, Alex Slivkins, Vasilis Syrgkanis) * Learning reductions: Every real-world learning problem is a little bit different from every other, so how can we solve them all without reinventing the field of machine learning for every problem? (Alekh Agarwal, John Langford, Rob Schapire) * Logarithmic-time prediction: Whether recognizing every face on the planet or choosing the optimal search result, efficiently choosing from a large set is critical for an effective learning algorithm. What techniques can accomplish this? (John Langford) * Online learning: How can we design machine-learning algorithms for sequentially-arriving data to achieve robust theoretical guarantees and an ability to deploy at scale? (Alekh Agarwal, Miro Dudik, John Langford, Rob Schapire) * Prediction markets: How can we use economic incentives to elicit information and aggregate beliefs from a pool of experts? (Miro Dudik, Jenn Wortman Vaughan) * Reinforcement learning: How can we learn to behave nearly optimally in a complex world with an evolving state? (Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, John Langford, Rob Schapire) * Structured prediction: What is a general-purpose approach to effectively and efficiently making a joint set of predictions (as in parsing, machine translation, etc.)? (Alekh Agarwal, Akshay Krishnamurthy, John Langford) For a list of recent publications, please see the MSR NYC Machine Learning website: http://research.microsoft.com/mlnyc Candidates for this position must have: * a PhD in computer science, electrical engineering, statistics, mathematics, or a related field; * a well-established research track record demonstrated, for example, by journal and conference publications, and participation on program committees, editorial boards, etc.; * strong communication skills; * ability to work in a highly collaborative and interdisciplinary environment; * for senior candidates, demonstrated leadership in their field. HOW TO APPLY To apply, submit an online application on the Microsoft Research Careers website: http://research.microsoft.com/en-us/jobs/fulltime/apply_researcher.aspx For full consideration, all materials, including reference letters, need to be received by January 1, 2016. In completing your application, please be sure to follow these additional instructions: 1. In addition to submitting your CV and the names of at least three referees, as required by the online application, please also upload the following three attachments: * two conference or journal articles, book chapters, or equivalent writing samples (uploaded as two separate attachments); * an academic research statement (approximately 3-4 pages) that outlines your research achievements and agenda. 2. Indicate that your research area of interest is "Machine Learning, Adaptation, and Intelligence" and that your location preference is "New York." Include "Robert Schapire" as the name of a Microsoft Research contact (you may include additional contacts as well). Note: IF YOU DO NOT MARK THESE PREFERENCES, IT IS VERY UNLIKELY THAT WE WILL RECEIVE YOUR APPLICATION. After you submit your application, a request for letters may be sent to your list of referees on your behalf. NOTE THAT REFERENCE LETTERS CANNOT BE REQUESTED UNTIL AFTER YOU HAVE SUBMITTED YOUR APPLICATION, AND FURTHERMORE, THAT THEY ARE NOT AUTOMATICALLY REQUESTED FOR ALL CANDIDATES. You may wish to alert your letter writers in advance so they will be ready to submit your letter by our application deadline of January 1, 2016. You can check the progress on individual reference requests by clicking the status tab within your application page. _______________________________________________ uai mailing list [email protected] https://secure.engr.oregonstate.edu/mailman/listinfo/uai
