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| Annual Tuition Fee: | ≈ € 4,815 - ≈ € 17,877 (non-EEA) | ||
| Location: | York / United Kingdom / View location on map ▾ Hide location on map ▴ | ||
| Duration: | 12 months | Start Date: | October |
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| Languages: | English | ||
Location of University of York
Natural Computation is computing inspired by the natural world, by biology, physics and chemistry. The MSc in Natural Computation aims to provide participants with a thorough grounding 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 or Computer Science/Mathematics joint honours who wish to develop knowledge and skills in this area before undertaking industrial work or academic study. Appropriate recent experience may also qualify you for the course, if you do not have an appropriate Computer Science degree.
The unique emphasis of the MSc Natural Computation course is on developing the computational view of natural processes, rather than considering particular aspects of nature-inspired computation, or concentration on the application of techniques in a particular domain.
Course aims
* to provide a broad education in applicable areas of natural computation and associated technologies
* to provide more specialised knowledge in natural computation technology via the project.
The MSc Natural Computation course is offered as a full-time MSc, running for 12 months from October, the start of the academic year. The first half of the course is taken up by taught modules. Each of the MSc Natural Computation modules comprise 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.
The taught modules are grouped 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). Some modules may only be taken if a pre-requisite module has also been taken. In addition, there is a (mandatory) module on writing and research skills.
Learning outcomes
The MSc Natural Computation 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.
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.
In particular, the MSc Natural Computation course is intended to provide a route into a PhD or research in this rapidly expanding field.
Contents
Course Content
The subjects taught will cover the following strands:
Bio-inspired computation: Algorithms for computation that have been inspired by observation of biological systems; simulation of complex biological systems.
Embodied computation: Physical and bio-chemical systems that are examined from a computational perspective.
Complexity and Emergence: understanding the properties of natural systems, and how these properties are related to the underlying structures.
Taken together these topics cover a broad range of natural systems, examining each from a computational perspective. In addition to the mandatory "Computer Science Writing" module, students must choose eight modules from the topics available. Students may bias their learning towards physical systems or biological systems, but will take a core of material in each area.
The components of the course are listed below:
Neural Algorithms
Algorithms inspired by natural neural systems.
Evolutionary Algorithms
Algorithms inspired by natural evolutionary systems.
Complex Dynamical BioSystems
Complex systems perspective of biosystems and the importance of the powerful central concepts of self-organisation and emergence
Quantum Information Processing
Theory of quantum information and quantum computation.
Cooperative Bio-Inspired Algorithms
Artificial immune systems; social systems; growth.
Computer Science Writing
Academic writing skills, project planning, literature research skills.
Emergence
Complex systems that exhibit emergent properties that cannot be reduced to behaviour of their individual components.
Evolvable Hardware
Provides a foundation of theoretical and practical knowledge of evolvable hardware.
Computing with Biology and Chemistry
Theory and practical knowledge in computational systems inspired by biological and chemical systems.
Quantitative Research Methods
An introduction to experimental design and statistics as used in HCI and computer science for the evaluation of interactive systems.
Project
Independent research project building on the taught course. Dissertation.
Personal Tutor
Each student is assigned to a tutorial group (usually containing no more than four or five students), and hence to a personal tutor who will monitor progression.
Assessment
Assessment of students' performance in the course modules will take place in a variety of forms: practical exercises, reports, closed examinations, open assessments and a dissertation for the project. Students are deliberately exposed to a variety of assessment methods so that they are not disadvantaged by background. Assessments will take place at various times during the year. Practical exercises, reports and other forms of open assessment will be due either during the course module or just after its completion.
Timescales, Modules and Project Descriptions may be subject to change.
You are normally required to take an English Proficiency Test if you come from a non-English speaking country.
Most European Universities recognise the IELTS test.
More informationRequirements
Entry Requirements
Typically applicants will have achieved at least a second class degree (or international equivalent) in Computer Science or a related discipline with an appropriate mathematical basis. We will consider applicants who do not have an appropriate Computer Science qualification but have compensatory experience, for example appropriate industrial experience.
Applicants are required to nominate two referees, of which at least one should be from the applicant's current employer or place of study. Applicants are normally interviewed before acceptance either in person if UK based or by telephone for international students.
English Language Requirements
The University's absolute minimum English language requirements are:
* IELTS: 6.5, with a minimum of 6.0 in each component
* TOEFL: paper-based 550/ computer-based (CBT): 213/ internet-based (iBT): 79
* Cambridge Certificate of Proficiency in English: A, B, C
* Cambridge Certificate in Advanced English: A
Language Proficiency
| Cambridge English: Advanced (CAE): | Grade A (Score: 80) |
Ask a Question
You can contact Keith Maynard to ask a question about Natural Computation at University of York.
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