Machine Learning Engineer (KTP Associate), CSEE

Updated: 19 days ago
Job Type: FullTime
Deadline: 14 Jun 2021

Machine Learning Engineer (KTP Associate), CSEE

School of Computer Science and Electronic Engineering

KTP

Knowledge Transfer Partnerships (KTPs) are government-funded collaborations between universities and businesses. In KTPs, academics and company representatives jointly supervise a KTP Associate who is based in the company, with the goal of improving their competitiveness and productivity. KTPs serve to make better use of the knowledge, technology and skills generated by universities, colleges and research organisations.

Further information is available at: http://ktp.innovateuk.org/

THE PROJECT

The University of Essex in partnership with CML Microcircuits (UK) Ltd offers an exciting opportunity to a graduate to combine skills in signal processing and machine learning and apply them to the next generation of low-rate speech communication systems.

This post is fixed term for 24 months and is based at CML Microcircuits offices in Langford, Maldon, UK. 

DUTIES OF THE POST

The duties of the post will include:

  • Devising an appropriate method of converting speech intelligibility into a dataset.
  • Applying machine learning techniques to speech signals and audio quality data sets.
  • Developing and applying appropriate algorithms for optimisation to maximise quality of speech.
  • Research into, evaluation and proposal of leading-edge techniques and algorithms across a broad range of knowledge areas.
  • Targeting models which can run in a computationally constrained environment, and to the requirements of a semiconductor design team; identify potential hardware accelerators as appropriate.
  • Pre-product development, documentation and testing.
  • Communicating technical concepts to stakeholders, including potential clients.
  • Carrying out lab tests and field trials and reporting/acting on findings.
  • Embedding technology and upskilling company staff.
  • Co-authoring articles in collaboration with academics at the University of Essex.
  • Participating in academic and/or industrial conferences and other events, to disseminate and present research outcomes to the wider community.

KEY REQUIREMENTS

The post holder must have:

  • Bachelor degree in Computer Science, Electronic Engineering, Telecommunications or a related discipline.
  • Knowledge of, or experience in, digital signal processing.
  • Experience with machine learning algorithms.
  • Experience in handling large and complex data.
  • Experience in programming languages such as C, Python or Matlab.
  • Ability to communicate highly technical information to non-technical colleagues and clients.
  • Ability to assess current state-of-the-art in digital signal processing and machine learning.
  • Ability to contribute to the drafting of an academic paper.

LOCATION

CML Microcircuits (UK) Ltd,

Oval Park, Hatfield Road,

Langford, Maldon,

Essex,

CM9 6WG

At the University of Essex, internationalism and diversity is central to who we are and what we do. We are committed to being a cosmopolitan, internationally oriented university that is welcoming to staff and students from all countries, faiths and backgrounds, where you can find the world in one place.

To support this commitment we have our Global Forum , a staff-led network that promotes and celebrates the rich cultural diversity among Essex staff, and our Colchester campus based Faith Centre , which hosts regular services, meetings and events organised by our chaplains and faith representatives.

Please see the attached job pack, which contains a full job description and person specification, which outlines the full duties, skills, qualifications and experience needed for this role plus more information relating to the post. We recommend you read this information carefully before making an application.  Applications should be made on-line, but if you would like advice or help in making an application, or need information in a different format, please telephone the Resourcing Team (01206 876559).

*More information: Working at the University


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