Sustainable cultivation modelling based on remotely sensed images for monitoring environmental consequences of climate change

Eligibility: UK/EU/International graduates with the required entry requirements

Funding details: Bursary plus tuition fees (UK/EU/International)

Duration: Full-time – between three and three and a half years fixed term

Application deadline: 15th March 2021

Interview dates: Will be confirmed to shortlisted candidates

Start date: September 2021 (Subject to discussion and agreement January 2022 may also be available)

For queries please contact Dr Sara Sharifzadeh.

GCRF PhD Studentships

This is a GCRF fully-funded PhD studentship.

See the full list of available opportunities

Introduction

Coventry University is inviting applications from suitably-qualified graduates for a fully funded PhD studentship in Artificial Intelligence (AI) and Computer Vision domain within the Centre for Data Science (CDS).

Coventry University has been voted ‘Modern University of the Year’ three times running by The Times/Sunday Times Good University Guide. Ranked in the UK’s top 15 (Guardian University Guide), we have a global reputation for high quality teaching and research with impact. Almost two-thirds (61%) of our research was judged ‘world leading’ or ‘internationally excellent’ in the Research Excellence Framework (REF) 2014.

CDS aims to develop cutting edge pure research in Artificial Intelligence, Data Science and Future Computing, linking fundamental science to real-world applications.

Data Science deals with the analysis and exploitation of large amounts of data (Big Data) drawing together disciplines as diverse as Computer Science, Artificial Intelligence, Statistics and Mathematics. The centre is organised according to three key themes:

  • Machine Learning for Big Data
  • Wireless Sensors and Internet of Things
  • Computational and Statistical Modelling
  • Advanced Computing

About the project

Climate change (CC) is one of the major global challenges that has significant influence on agricultural productivity. The focus of this project is on utilisation of Artificial Intelligence (AI), satellite image analysis and other measured or remotely sensed data to understand the spatio-temporal cultivation patterns and identify its driving environmental factors to develop a climate-adapted cultivation model.

Modelling cultivation based on various environmental factors extracted either from remotely sensed data or from real world measurement, is a multidimensional challenge and a cross disciplinary research. The results of such modelling strategy will give new valuable insights about the climate-adapted cultivation. The results of this study can inform the agricultural adaptation policies made by governments, digital agriculture businesses and other stakeholders. The benefits in a larger scale will be resilience and improved sustainable societies.

The three main research objectives are:

  1. To model crop yield based on time series analysis of satellite images and other remote sensing data and measured environmental factors.
  2. To identify the optimum set of environmental factors including the remote sensing and measurement data, that can improve the crop prediction models
  3. To develop a new adaptive cultivation modelling strategy capable of predicting optimum transitions in cultivation patterns for sustainable crop yield in highly dynamic condition induced to inform CC mitigation and preventative plans

Funding 

Fully funded single studentship, which includes tuition fees and living expenses for a doctoral candidate over 3.5 years.

Stipend rates will be equivalent to those set by UKRI and will rise annually with a projected average increase of 1.25% per year. CU fees are adjusted yearly in line with the rates set by UKRI.

Benefits

Considering the induced global challenges by CC, this project addresses an important problem about CC. On completion of this project, a better understanding will be gained about the most important environmental factors, influencing cultivation productivity in semi-dried area due to CC.

In addition, the achievements of this project can inform the agricultural adaptation policies made by governments, digital agriculture businesses and other stakeholders.

On the other hand, the project strengthens the PhD student skills in AI and image analysis that support the candidate for future career.

Furthermore, Coventry University will benefit from this cutting-edge research project by generating three journal publications in high-impact journals, e.g. Elsevier and IEEE Transactions. This will helps the University to meet its target in the UK Research Excellence Framework (REF) also improve its position globally.

  • Our research strategy is underpinned by a £250m investment in research and facilities
  • Dedicated Doctoral College and Centre for Research Capability Development deliver high quality professional support for researchers, from PhD to Professor
  • Free training: research career planning, managing your doctorate, research communication skills, research ethics, research impact, research integrity, research methods and research supervision
  • Coventry is a member of the Doctoral Training Alliance (DTA), the largest multi-partner and only nationwide doctoral training initiative of its kind.
Training and Development

The successful candidate will receive comprehensive research training including technical, personal and professional skills.

All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.

Candidate specification

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average. 
  • In the event of a first degree classification of less than 2:1, a Masters degree in a relevant subject area will be considered as an equivalent. The Masters must have been attained with overall marks at minimum merit level (60%). In addition, the dissertation or equivalent element in the Masters must also have been attained with a mark at minimum merit level (60%).

or

  • a taught Masters degree in a relevant discipline, involving a dissertation of standard length written in English in the relevant subject area with a minimum of a merit profile: 60% overall module average and a minimum of a 60% dissertation mark
    PLUS 
    the potential to engage in innovative research and to complete the PhD within a 3.5 years
  • a minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)
In addition

A good knowledge of

  • linear algebra, machine learning and AI concepts, linear/non-linear systems, and willingness to quickly learn further in this area
  • Digital image analysis and multivariate data analysis skills
  •  Good programming skills in MATLAB/Python
  • Good writing and interpersonal/communication skills

How to apply

All applications require full supporting documentation, and a covering letter – plus an up to 2000-word supporting statement is required showing how the applicant’s expertise and interests are relevant to the project.

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