PhD in Networks, Distributed systems and Algorithms

Topics: The following are some sample research areas, and are all mutually related. You are welcome to work on any combinations of them.

Mobile, wireless and sensor networks

Our goal is to use the location technologies and sensors to detect the circumstances of a user for better application performance. Where are the users? What are they doing? How can we find that and deliver better services and information to them? These are challenging to do in low power phones and sensors, and hence an intriguing research area.

Distributed Computing

Large datasets -- "Big Data" -- cannot be analyzed using a regular computer. They must be processed using many computers in "clusters" or "data centaers." However, traditional methods of optimization, data mining and learning assume computations on a single computer. How can we distribute the computations to process data on many computers?

Social & complex networks

Many large datasets are either networks or closely related to networks. For example, social networks, professional networks, biological networks, the internet etc. What information can we gain from these networks? Can a social network help us predict which event or movie will be popular? Can we analyze the internet and say how it should be modified for better performance? What are the properties of networks that make them effieicent or inefficient in certain tasks?

Computational geometry and topology

Location is a commonly available information about people, data and events, and thus constitute an important attribute in mobile systems, big data and complex networks. Computational geometry is about making use of locations and other coordinate systems for better computations in mobile, distributed or network settings.

Algorithms

Algorithms are the most fundamental elements in computer science. We are intereseted in algorithms relevant to all the areas above. In the modern age of massive datasets and many different types of devices, we need algorithms that can operate rapidly, producing good results. Thus we are looking for approximations and online, streaming algorithms.



POSITIONS:

The Univeristy of Edinburgh hosts multiple CDTs covering most aspects of computer science. The CDTs provide 4 years of support. You can work with me in either:

For students Interested in Systems:

EPSRC Center for Doctoral Training on Pervasive Parallelism

For students interested in big data issues like data mining, analysis etc:

EPSRC Center for Doctoral Training on Data Science

For students in interested in theory, algorithms, geometry etc, both the CDTs above are relevant.

In addition, you can also choose to do a regular Ph.D. outside of CDTs.

You can apply with or without a masters degree.

If you are not sure which option is best for you, please contact me. In fact, you should contact me anyway before applying. See below.


To apply,

  1. Send CV and transcripts to Dr Rik Sarkar by email with subject “PhD in Networks” and with an explanation of your interests.

  2. When instructed to do so, submit formal application at: The PhD application page for Informatics, U. Edinburgh. You can choose to apply for one of two Labs: either the laboratory for foundations of computer science or the Institute for Computing Systems Architecture. Or to a CDT.

  3. The deadline for the second step is December 15 for international students. So please start your process early. Other students are also encouraged to get in touch sooner rather than later.

For the formal application you will need to write a research statement describing your interests, and will have to submit contact information for 2 referees who will be asked for letters of recommendation.

Funding Full funding for 3 years is available for UK and EU students, covering tuition, plus a stipend at the standard EPSRC rate. Partial funding is available for overseas students.

The University of Edinburgh is one of the top 20 in world in the  QS rankings. The Edinburgh School of Informatics is the largest in UK and has consistently ranked as the top in research. The historic city of Edinburgh is lively, beautiful, and a great place to live. More information for students are available at here and here