KTN's online platform helps you to make the connections you need


The Knowledge Transfer Network (KTN) has refreshed its online platform to intelligently connect you to relevant events, funding, thought pieces and specialist staff to help your business innovate and grow.

You can discover content using your area of interest, from ICT to transport; from space to health – all major UK economic sectors are covered. Once you have selected your interests, using our intelligent tagging system, we will then display rich and relevant content related to your area, often from surprising sources.

An example might be new satellite technology from the space sector that is applicable in the agri-food sector. KTN-UK.co.uk will help you form these unusual and valuable connections.

All content on the platform has been carefully curated by our team of innovation specialists – not by an automated algorithm – so you can be confident that KTN is connecting you to the most relevant cutting-edge information.


The move also marks a closer alignment with our main funder, Innovate UK , with the website branding making a clear visual link. Knowledge Transfer Network is Innovate UK's innovation network partner, and also works with other funders to provide innovation networking services and fulfil our mission to drive UK growth.

We link new ideas and opportunities with expertise, markets and finance through our network of businesses, universities, funders and investors. From agri-food to autonomous systems and from energy to design, KTN combines expertise in all sectors with the ability to cross boundaries. Connecting with KTN can lead to potential partners, horizon-expanding events and innovation insights relevant to your needs.

Visit our people pages to connect directly with expertise in your sector.

Visit the KTN refreshed online platfom here

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Inference for Change-Point and Related Processes

Inference for Change-Point and Related Processes
Isaac Newton Institute, Cambridge
7th-11th January 2014

In many applications data is collected over time or can be ordered with respect to some other criteria (e.g. position along a chromosome). Often the statistical properties, such as mean or autocovariance, of the data will change across the data. This feature of data is known as non-stationarity. An important and challenging problem is to be able to model and infer how these properties change.
This workshop will focus on recent work on modelling and inference for such data, focusing on changepoint and locally-stationary models.

Keynote Speakers:
Rainer Dahlhaus (Heidelberg)
Guy Nason (Bristol)
David Siegmund (Stanford)

For full details, see the workshop web-page.

Registration is £200 (£155 for students).

To apply to attend this workshop complete the online form (Deadline of 1st October):

Application deadline: 1st October 2013.

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All Day
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Venue :
Isaac Newton Institute for Mathematical Sciences
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Isaac Newton Institute

20 Clarkson Road


United Kingdom


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