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KTP Associate – Machine Learning Engineer
Not on campus

Job reference: KT20005

Location: Not on campus

Closing date: 29/11/2020

Job live date: 30/10/2020

Salary: £38,000 - £42,000 pa depending on qualifications and experience

Employment type: KTP Associate

Department: Commercial

Division: Computer Science

Hours per week: 36

Employment Basis/Type: Fixed Term for 24 months

The University of Reading and CrowdCharge are delighted to announce they are working collaboratively on an exciting Knowledge Transfer Partnership (KTP) to improve the performance of CrowdCharge’s novel Smart Electric Vehicle Charging Platform. This will enable the business to offer a competitive, market-leading service as it scales, bringing cost savings to its customers through optimised vehicle charging.   The position is a 24-month fixed term contract based full-time at CrowdCharge in Marlow, Bucks with support from experts at the University of Reading. 

About Crowdcharge:

CrowdCharge operates a digital platform to manage multiple electric vehicle chargers to provide EV owners with cheaper and greener electricity, while at the same time reducing the impact from EV charging on the electricity grid – in the UK and globally. 

About KTP:

This position forms part of the Knowledge Transfer Partnership (KTP) funded by Innovate UK.  It’s essential you understand how KTP works with business and the University, and the vital role you will play if you secure a KTP Associate position.

Skill development through training:

There is a £4k dedicated training budget tailored towards your own personal development in addition to residential management skills training. 

The successful candidate must possess:

•    Minimum of 1st class BSc in Machine Learning, Data Science,  Statistics/Applied Mathematics or related Computing disciplines 
•    Demonstrable experience using Machine Learning and statistical models
•    Demonstrable experience of software development, including UML or SQL, and a programming language, such as Python, to
      solve numerical problems or analyse systems

 Essential skills we require as a minimum:

​​​​​​•    Excellent communication skills and time management
•    Able to work independently, but also confident working in a team with a diverse skillset and from diverse disciplines
•     Confident working with people at all levels - including senior managers, customers, colleagues and suppliers
•    An interest in developing a career in the smart energy sector and/or the development of AI-enabled systems

​​​​​​​Interview date: 10/12/2020

Informal contact details

Alternative informal contact details

Contact role:

Academic Supervisor

Contact role:

Academic Lead

Contact name:

Varun Ojha

Contact name:

Etienne Roesch

Contact phone:

0118 378 8609

Contact phone:

0118 378 8581

Contact email: 

Contact email:

The University is committed to having a diverse and inclusive workforce, supports the gender equality Athena SWAN Charter and the Race Equality Charter, and is a Diversity Champion for Stonewall, the leading LGBT+ rights organisation. Applications for job-share, part-time and flexible working arrangements are welcomed and will be considered in line with business needs.


The closing date for this job has now passed.


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