Postdoc position in algorithms (closing March 31st 2016)

We are seeking to recruit a postdoctoral research fellow to work in the area of designing algorithms for analysing large data sets.

You will be expected to perform high quality research under the supervision of Professor Graham Cormode, as part of the ERC funded project ‘Small Summaries for Big Data’. This can encompass streaming algorithms, sketching and dimensionality reduction, distributed monitoring and mergeable summaries, verification of outsourced computation, or other related topics. The expectation is that you will produce breakthrough research results in the summarisation of large volumes of data, and publish these results in top rated venues.

You will possess a PhD or an equivalent qualification in Computer Science or a very closely-related discipline (or you will shortly be obtaining it). You should have a strong background in one or more of the following areas: randomized and approximation algorithms; communication complexity and lower bounds; streaming or sublinear algorithms.

The post is based in the Department of Computer Science at University of Warwick, but collaborations with closely related research organisations such as the Centre for Discrete Mathematics and its Applications (DIMAP), the Warwick Institute for the Science of Cities (WISC); and the newly formed Alan Turing Institute (ATI) will be strongly encouraged. You will join a team of researchers led by Professor Cormode including other postdoctoral researchers and PhD students.

Candidates should provide with their application form a CV, a list of publications and a research statement.

Closing date: 31st March 2016

More information:

ERC Consolidator Grant

Graham Cormode has been awarded a prestigious ERC consolidator grant worth 1.5M euro, to support his research.  The grant is for a project entitled “Small Summaries for Big Data”. The project focuses on the area of the design and analysis of compact summaries: data structures which capture key features of the data, and which can be created effectively over distributed data sets. The project will substantially advance the state of the art in data summarization, to the point where accurate and effective summaries are available for a wide array of problems, and can be used seamlessly in applications that process big data.