Machine Learning Scientist

Machine Learning Scientist
  • Competitive
    ANNUM
  • Temporary
  • Bracknell, Berkshire, England, UK Bracknell Berkshire UK RG42 6EY
Job Ref: J2163306
Sector: Science
Sub-sector: Environmental
Date Added: 25 January 2022
SRG are currently looking for a Machine Learning Scientist based in Bracknell.

Job Title: Machine Learning Scientist (Plant Ecology and Evolution Modelling)

Location: Bracknell, Berkshire

Contract: 3 years

Pay Rate: Competitive

SRG is working with a global agrochemical company in Berkshire who are helping to improve global food security by enabling millions of farmers to make better use of available resources more sustainably. They are currently seeking a Machine Learning Scientist with proven experience in numerical sciences and have a sound understanding of biological and environmental systems. This will be an excellent opportunity for a motivated scientist to model weed populations to predict the evolution of herbicide resistance, as affected by environmental factors, genetic processes and farming practices, including herbicide use patterns. As an R&D based company, this organisation has high standards in science quality and innovation is at the heart of their culture. The industry environment provides a unique opportunity for you to apply cutting-edge science to make a real difference in how farmers grow food in the future and meet sustainability challenges with new tools and solutions. As a researcher, attendance in international conferences and publication in peer-reviewed journals is encouraged and supported. In addition, you will have the opportunity to collaborate with universities and academic institutions through company funding.

Responsibilities:

  • You will model weed populations to predict the evolution of herbicide resistance, as affected by environmental factors, genetic processes and farming practices, including herbicide use patterns.
  • Confident to work independently, you will build and apply predictive models to provide information to R&D projects, support commercial activities and co-develop digital tools for customers.
  • As a modeller, your core activities include data analysis, model construction, validation, optimisation, and documentation.
  • You will implement advanced modelling techniques to improve and scale existing predictive models and make better use of environmental and agricultural data.
  • You will report to a senior modeller and work in a multi-disciplinary environment, interacting with weed scientists, biologists, data scientists, agriculture experts and commercial colleagues.
  • You will play a key role in steering data generation activities and influence how predictive science is applied to support the business.

Requirements:

  • PhD (or equivalent experience) in numerical sciences, such as data science, machine learning, physics, mathematics, statistics, computer science, or alike, and have a good understanding of biological and environmental systems.
  • Have practical experience of developing models to support decision making.
  • Strong mathematic and statistic skills.
  • Proven programming experience, ideally with Python, or R/ C++/Java/MATLAB, and familiar with relevant libraries.
  • Comfortable working with complex and suboptimal datasets.
  • Experience with agent- or individual-based models and/or GIS data analysis would be beneficial but not essential.
  • Track record of publication in peer-reviewed journals.
  • Good communication skills, with the ability to relate information to different audiences and communicate complicated model algorithms in layman's terms.
  • Previous experience with modelling biology, ecology or environmental systems would be favourable.
  • Excellent attention to detail and rigorous approach to data analysis, debugging and model documentation.
  • Good planning and organisation skills.

Apply online today. This position will be open until filled. If you have any questions about the application submission process please contact Rabia at rabia.khan@srgtalent.com

SRGTalent
24/02/2022 11:52:24
GBP 0.00 0.00 Annum
Contact Consultant:
Rabia Khan

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