Research Assistant/Associate in Plant Phenotyping at Newcastle University

Research Assistant/Associate in Plant Phenotyping

Requisition ID:  5358
Location:  

Newcastle, GB

Contract Type:  Fixed Term
Working Pattern:  Full Time
Posted Date:  

 

We are a world class research-intensive university. We deliver teaching and learning of the highest quality. We play a leading role in economic, social and cultural development of the North East of England. Attracting and retaining high-calibre people is fundamental to our continued success.

 

Salary:  Research Assistant: £28,331 – £30,046 per annum

Research Associate: £30,942 – £38,017 per annum

 

Closing Date: 26 November 2020

 

The Role

 

We are looking for a Research Assistant/Associate in Plant Phenotyping to join our team to develop methodological approaches and engineering applications of optical and spectral sensing-based plant phenotyping.

 

Vertical farming is one of the fastest growing innovations in farming, benefiting from advances in LED lighting technologies, sensors, environmental control and connectivity (IoT technologies). Vertical farming approaches seek to improve the efficient use of resources to increase production of high-value crops, whilst at the same time imbedding food production efficiently in the supply chain to avoid waste, reduce transport costs (and emissions) and increase sustainability.  The aim of the project is to develop phenotyping pipeline and automate plant monitoring/screening for variable stresses using photometric-stereo and spectral based sensors under controlled vertical farming environment. This will involve plant phenotyping, image analysis, deep learning and statistics/mathematical modeling based combined approaches to support early stress detection, crop monitoring and improving agricultural precision management

 

You will join a team of researchers working in partnership with InFarm, Europe’s leading vertical farming company, who operate a netwok of in-store farms for 25 major European retail chains, including Marks and Spencer who are also a member of the project team. We will work in partnership with Roboscientific who have developed innovate tools for the real-time detection of VOCs, ideally suited to vertical farming systems.

 

For informal enquiries, please contact Dr Ankush Prashar, ankush.prashar@ncl.ac.uk

 

The post if full time, fixed term for the duration of 29 months.

 

For information on the group you will be a part of, please click here.

 

Key Accountabilities

 

  • To contribute to an internationally visible research profile in plant phenotyping and image analysis team, both independently and as a member of the Agricultural Production Systems Group under the clear guidance of a member of an academic staff
  • To work under the general guidance of a member of academic staff contribute ideas, including enhancements to the technical or methodological aspects of their studies, providing substantial ‘added value’
  • To use initiative and creativity to analyse and interpret research data and draw conclusions on the outcomes. Assess research findings for the need/scope for further investigations
  • To co-ordinate own work with that of others, deal with problems which may affect the achievement of research objectives and contribute to the planning of the project(s)
  • To present information on research progress and outcomes to the project PI and other consortium partners and project meetings
  • To present research findings, either at conferences (nationally and internationally) or through publications in appropriate journals and grey literature
  • To work to deadlines and manage, with support, competing priorities
  • To ensure that personal knowledge in relevant fields of study is kept up to date
  • To use research resources (including, where required, laboratories, workshops and specialist equipment) as appropriate
  • To maintain academic standards and freedom, and work in accordance with university policies (e.g. equal opportunities, health and safety policies)
  • To supervise the laboratory work of postgraduate research students (MSc and PhD) and undergraduate project students
  • Possible demonstration duties within undergraduate laboratories and classes
  • To undertake relevant training and development activities to develop capacity for taking on wider responsibilities
  • To develop, with advice, an awareness of own professional development needs and a personal development strategy
  • To participate in and contribute to the development of a vibrant research culture within the school

 

The Person (Essential)

 

Knowledge, Skills and Experience

  • Programming skills (e.g Python, Matlab etc)
  • Knowledge of analytical/statistical (data mining) techniques in image analysis
  • Working knowledge of spectral and image analysis for agricultural applications
  • Knowledge of remote sensing techniques
  • Postdoctoral experience in image analysis, artificial intelligence and/or biological modelling
  • A track record of high-quality scientific manuscript preparation and publication
  • Computer programming/coding
  • Working with spectral datasets, statistical analysis and modelling
  • Knowledge/Understanding of crop management and physiological processes
  • Use of tools for spatial data, GIS
  • Integrative skills with a willingness to work as part of a team
  • Ability to work at pace and to deadlines
  • Ability to communicate research results to a range of audiences (particularly within industry and/or policy) in appropriate formats

 

Attributes and Behaviour

  • Highly developed planning and organisational skills
  • Effective time management and prioritisation skills
  • High level of analytical and problem-solving capability with personal skills, enthusiasm, inititative and drive to achieve the solution
  • Good team working and networking skills and the ability to build productive links with fellow professionals in other institutions, companies and agencies
  • Demonstrable commitment to our values
  • Professionalism and Academic Excellence
  • Transparency and Accountability
  • Equality, Diversity and Inclusivity
  • Collaborative and Multi-disciplinary
  • Ambition and Aspiration
  • Motivation and Passion
  • Inquisitive, Creative and Imaginative
  • Integrity and Respect
  • Social Responsibility

Qualifications

  • PhD (Associate level) (or near completion for Assistant level) in engineering, statistics, computer science, physics, mathematical biology, plant physiology or relevant discipline with strong background in macine learning

 

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