MSc in Natural Computation

Information for Current Students

The programme aims to equip students with knowledge, understanding and practical application of a broad range of components of Natural Computation, to complement previously gained knowledge and skills in traditional Computer Science.

Introduction

The MSc in Natural Computation aims to provide participants with a thorough grounding, and practical experience, in the use of advanced techniques of natural computation - broadening ideas about computation to include ideas from mathematics, physics, electronics and biology. It is aimed at graduates with a first degree in Computer Science, Computer Science/Mathmatics joint honours, or equivalent experience, who wish to develop knowledge and skills in this area before undertaking industrial work or academic study. Graduates completing the course will be equipped to play leading and professional roles in natural computation related aspects of industry, commerce, academia and public service. This advanced level course is built on research work at York, and is supported by a number of industrial organisations including Microsoft, BAE Systems and QinetiQ.

Course objectives:

The course is offered as a full-time MSc, running for 12 months from the start of October. The first half of the course is taken up by taught modules. Each module comprises a mixture of lectures, problem classes and practical classes plus a significant amount of personal study time. In the latter half, students undertake an individual research project, under the supervision of a member of staff. In addition, each student is a member of a tutorial group which meets regularly with a supervisor.

The taught modules are organised into three strands: Bio-inspired Computation (neural networks, genetic algorithms, artificial immune systems, simulation of complex biosystems), Embodied Computation (quantum computation, evolvable hardware, DNA computing), and Complexity and Emergence (dynamical systems, adaptive and learning agents, emergent behaviour).