Machine Learning - KTP Associate

£33,000 - £35,000

Leiston, Suffolk - 24 months

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Job description

* 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.

Project description

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.

About the business

Soil Moisture Sense is one of the UK’s leading providers of crop data and decision support services, with over 30 years of experience in crop monitoring. Our wide range of solutions for crop and weather monitoring are backed by expert data analysis and near real-time updates. We provide an end to end service, including system builds, installation, online date display, daily emails, on-site visits, and regular maintenance and repairs as required.

Agritech

Electronic Engineering

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

2 August 2022

30 August 2022

1 November 2022

REQ06291


* 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.