Computational Perception Group

home | members | research | publications | presentations | teaching | vacancies | personal

Vacancies

PhD positions

The group has openings for students who want to study for a PhD in either the biological aspects of our research programme (see here for application details) or the machine learning and signal processing aspects (see here for application details).

If you want to find out more about the core technical material that is relevant to the Group's reseach, please see the Group's recommended reading list.

Postdoctoral positions

We are currently advertising a Research Assistant/Associate position:

Research Assistant/Associate in Machine Learning for Time-Series Data (Fixed Term)

Applications are invited for a Research Assistant/Research Associate in the Department of Engineering to work on probabilistic machine learning for time-series applications. The post holder will work with Dr. Richard Turner in the Machine Learning Group. The group also includes Prof. Zoubin Ghahramani and Dr. Carl Rasmussen who are advisors on this project.

The post will involve theoretical work that will develop a new probabilistic machine learning framework for time-series models. The post will also involve running computational simulations to evaluate the new methods in application areas including, but not limited to, audio processing. The project is sponsored by an EPSRC First Grant award and will start in early May 2014, or as soon as possible thereafter.

The post holder will be located in Central Cambridge Cambridgeshire, UK.

The key responsibilities and duties are to contribute to the development of a new theoretical framework at the interface of machine learning and signal processing. To implement and evaluate the new framework in application domains that may include denoising, missing data-imputation and audio source separation.

The candidate will have experience in both theoretical and practical aspects of audio signal processing and probabilistic machine learning, as evidenced by a publication record in the main conferences and/or journals of the field. Will continually update knowledge in this specialist area and engage in continuous professional development. Will have good oral and written communication skills, also experience of working in a team and managing own work load.

The role holder will possess some research experience with sufficient breadth/depth of specialist knowledge in the discipline and of research methods and techniques to work within established research programmes.

The candidate should hold a PhD?, or be near to completion of a PhD?, in a relevant specialist subject such as machine learning, signal processing, or statistics.

Salary Ranges: Research Assistant: 24,289 - 27,318. Research Associate: 28,132 - 36,661.

Fixed-term: The funds for this post are available for 12 months in the first instance.

Once an offer of employment has been accepted, the successful candidate will be required to undergo a health assessment.

To apply online for this vacancy, please click on the 'Apply' button below. This will route you to the University's Web Recruitment System, where you will need to register an account (if you have not already) and log in before completing the online application form.

Please ensure that you upload your Curriculum Vitae (CV), a statement of research interests and a covering letter in the Upload section of the online application. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application. Please submit your application by midnight on the closing date.

If you have any questions about this vacancy or the application process, please contact Miss Diane Hazell (dsu21@cam.ac.uk), Department of Engineering, Trumpington Street, Cambridge, CB2 1PZ, (tel: +44 (0)1223 748529),

Please quote reference NM03006 on your application and in any correspondence about this vacancy.

The University values diversity and is committed to equality of opportunity.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

 
Best viewed with Firefox 3, Safari 4 or Explorer 8. 2009 Computational & Biological Learning Lab, Department of Engineering, University of Cambridge