Postdoctoral Research Associate/Assistant Positions in Machine Learning - UNIVERSITY OF CAMBRIDGE
We are seeking highly creative and motivated postdoctoral Research Associates/Assistants to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK, working with Professor Zoubin Ghahramani. The group is one of the world's leading centres for Bayesian statistical Machine Learning and successful candidates will be expected to have a strong publication record in this field. Specific areas where we are recruiting include:
- Advanced Bayesian Computation for Cross-Disciplinary Research. The aim of this project is to develop novel advanced algorithms for probabilistic modelling applicable across a range of physical, biological and social sciences.
- Research in areas related to graphical models, statistical time-series modelling, sampling methods, approximate inference, and Bayesian nonparametrics.
- Scalable unsupervised probabilistic modelling for Big Data problems.
The positions are available now and can start as soon as the successful applicant is appointed.
The successful applicant will have or be near completing a PhD in computer science, engineering, statistics or a related area, and will have extensive research experience and a strong publication record in statistics, probability, or machine learning. Preference will be given to applicants with some experience in large-scale modelling with Bayesian methods, non-parametric Bayesian models, and approximate inference.
To apply complete form CHRIS /6 (cover sheet for C.V.s) available at:http://www.admin.cam.ac.uk/offices/hr/forms/ and send with your C.V. which should include a list of your publications and names of at least two referees, and a covering letter indicating which area you wish to be considered for, in pdf format by email to Diane Unwin, (email email@example.com , Tel +44 01223 748529).
Applications should be sent so as to reach us by 15th February 2013. Shortlisting will happen soon thereafter.
Quote Reference: NA24722.
Interview Date(s): Interviews will be held with selected candidates as soon as possible after the closing date.