Connectionists: ** Part-time option opens ** -- MSc in Computational Cognitive Neuroscience

[Max Garagnani]

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The MSc in Computational Cognitive Neuroscience programme at Goldsmiths, University of London (UK) is now accepting application for 2020-21 entry.

 

** PART-TIME OPTION NOW OPEN ** 

 

We are delighted to announce the opening of a part/time option for the programme.

Applications for full- and part-time study with entry in September 2020 and September 2021 are now open.

To submit an application, please click on this link and follow the instructions. NOTE: places on this course are limited and allocated on a first-come first-served basis. Apply now to avoid disappointment later.

 

 

COURSE OUTLINE:

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This is a one-year full-time or two-years part-time MSc degree programme, consisting of taught courses (120 credits) plus research project and dissertation (60 credits). It is designed for students with a good degree in the biological / life sciences (psychology, neuroscience, biology, medicine, etc.) or physical sciences (computer science, mathematics, physics, engineering), however, individuals with different backgrounds but equivalent experience will also be considered.

(Note: students requiring a Tier-4 VISA to study in the UK are restricted to the full-time pathway of the programme).

 

The course is a combined effort of the Computing and Psychology Departments and builds on the multi-disciplinary and strong research profiles of our staff. It equips students with a solid theoretical basis and knowledge of experimental methods in computational cognitive neuroscience, providing them also with an opportunity to apply their newly acquired knowledge in a practical research project, which may be carried out in collaboration with one of our industry partners (see below). 

 

The core contents of this course include (i) fundamentals of cognitive neuroscience (cortical and subcortical mechanisms and structures underlying cognition and behaviour, plus experimental and neuroimaging techniques), and (ii) concepts and methods of computational modelling of biological neurons, simple neuronal circuits, and higher brain functions. Students are trained with a rich variety of computational and advanced methodological skills, taught in the four core modules of the course (Modelling Cognitive Functions, Cognitive Neuroscience, Cortical Modelling, and Advanced Quantitative Methods). Unlike other standard computational neuroscience programmes (which focus predominantly on modelling low-level aspects of brain function), one of the distinctive features of this course is that it includes the study of biologically constrained models of cognitive processes (including, e.g., language and decision making). The final research project can be carried out ‘in house’ or in collaboration with an external partner, either from academia or industry. 

 

* INDUSTRY LINKS *

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The programme benefits from an ongoing collaborative partnership with representatives from 6 different international companies with headquarters in UK, Germany, Italy, USA and Japan. Carrying out your final project in collaboration with one of our industry partners will enable you to acquire modern technical skills that are much in demand, paving the way for employment opportunities and making you particularly competitive on the job market.

 

For any further information, including funding opportunities and tuition fees, please visit:

https://www.gold.ac.uk/pg/msc-computational-cognitive-neuroscience/

https://www.gold.ac.uk/pg/fees-funding/

 

For any other specific questions, please do not hesitate to get in touch.

 

Dr. Max Garagnani

Dr. Maria Herrojo Ruiz

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Joint Programme Leaders, MSc in Computational Cognitive Neuroscience

Department of Computing 

Goldsmiths, University of London

Lewisham Way, New Cross

London SE14 6NW, UK

Tel. +44 (0)20 7919 7850 

Fax: +44 (0)20 7919 7853 

https://www.gold.ac.uk/computing/people/garagnani-max/

https://www.gold.ac.uk/psychology/staff/herrojo-ruiz-maria/

 

Follow this Masters on Twitter: https://twitter.com/coconeuro_gold 

 

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