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Machine Learning KTP Associate

University of Essex - School of Computer Science and Electronic Engineering (CSEE)

Location: Leiston
Salary: £36,000 per annum
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 7th December 2023
Closes: 9th January 2024
Job Ref: REQ08148
 

KTP

Knowledge Transfer Partnerships (KTPs) are a unique UK-wide activity that help businesses to improve their competitiveness and productivity by making better use of the knowledge, technology and skills within universities, colleges and research organisations. Further information is available at: www.ktponline.org.uk.

THE PROJECT

The University of Essex in partnership with Soil Moisture Sense offers an exciting opportunity to apply intelligent automation to soil moisture data analysis, decision support and system maintenance prediction using the latest techniques in machine learning and data science.

This post is fixed term for 18 months and is based at Soil Moisture Sense offices in Leiston, Suffolk.  

DUTIES OF THE POST

The duties of the post will include:

  • Understanding the structure of Soil Moisture Sense and available software/hardware tools.
  • Performing data collection and pre-processing using signal processing techniques.
  • Designing an AI based predictive model for soil moisture monitoring using machine learning methodologies.
  • Investigating decision support systems and machine learning approaches suitable for crop and weather monitoring.
  • Embed technology and upskilling company staff.
  • Participate in academic and/or industrial conferences and other events, to disseminate and present research outcomes to the wider community.
  • Work towards the publication of peer-reviewed articles in high impact Journals in collaboration with academics at the University of Essex.

KEY REQUIREMENTS

  • Bachelor’s degree in computer science, Mathematics or a related discipline.
  • Master’s in computer science, Data Science, Mathematics or a related discipline or equivalent industry experience.
  • Experience developing and deploying machine learning algorithms.
  • Familiarity with relevant techniques such as recurrent neural networks, Gaussian Processes and semi-supervised machine learning.
  • High-level programming skills in a relevant language used for data science such as Python or Java.
  • Experience and knowledge of best practice in capturing and storing data from sensors.
  • An interest in agriculture, food security, and the challenges facing UK farmers.
  • The ability to understand commercial drivers and apply this contextual awareness to a technical project.
  • A good communicator able to relate technical concepts to non-experts.
  • A collaborative approach and a good team player.
  • Ability to take the initiative and work unsupervised where necessary.
  • Ability to manage a complex project with competing priorities and deadlines.
  • A meticulous approach to documenting work.

BENEFITS

As a KTP Associate, the post will offer the following benefits:

  • A personal development budget of £3,900 (exclusive of salary).
  • A travel and subsistence budget of £2,400, and a consumables budget of £2,200 (exclusive of salary).
  • Management training and mentoring by an Innovate UK KTP Adviser.
  • An interesting and challenging role, with exposure to a variety of stakeholders.
  • Full access to university resources to complete the project.
  • World-leading Academic and Company project supervision, with project support by a dedicated, sector leading KTP Office.

LOCATION

Soil Moisture Sense Ltd
Cloverlea Nuttery Lane
Leiston
Suffolk
IP16 4QW

Hybrid working offered.

Please use the 'Apply' button to read further information about this role including the full job description and person specification which outlines the full duties, skills, qualifications and experience needed for this role. You will also find details of how to make your application here. Our website http://www.essex.ac.uk contains more information about the University of Essex. If you have a disability and would like information in a different format, please email resourcing@essex.ac.uk.

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