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Develop accurate dynamical predictive models of key biosphere components for our revolutionary new Planetary Intelligence Platform. You will work with a diversity of science-based mechanistic dynamical models and heterogeneous ecological and environmental datasets. Work will span the full life cycle of product development, from initial research and design to product delivery and maintenance, although initial emphasis will likely be on conception and development. This role will be highly collaborative, working with the engineering, science and business development leads (most of which are ex-Microsoft) as well as external corporate partners.
Permanent or fixed-term post-doctoral.
Highly competitive salary, share option scheme, medical insurance.
Hybrid remote-physical. Physical meetings London, UK.
Responsibilities
Identify, encode and train dynamical predictive models of key biosphere components.
Identify and process necessary environmental datasets (e.g. Training and driver data).
Collaborate with internal and external stakeholders to identify, test and refine requirements.
Collaborate with the firm’s and our partners and customers’ software and data engineers to ensure appropriate incorporation of biosphere components into the platform.
Qualifications
PhD in appropriate computational ecology/science area.
Post PhD experience in the development and application of predictive and prescriptive ecological models for practical applications.
Experience in training predictive dynamical models of ecological systems (e.g. Crops, soils, forests, hydrology, epidemics).
Experience working with a diversity of ecological and environmental datasets as part of an analytical pipeline.
Excellent communication skills.
(Desirable) Experience encoding analytical workflows in a range of coding languages: .NET (F#, C#), Python, R.
About our company
Scientific Technologies is a new kind of science-based technology startup, developing a portfolio of radical impact technologies for the world to come. One such technology is our Planetary Intelligence Platform: software that empowers businesses to effectively integrate business information with information about key systems external to their business (climate, carbon, hydrology, ecosystem structure and function) to bring critical intelligence to business-critical decisions. Core to the platform will be an ability to deliver accurate predictive intelligence about business impacts on and from ecological and climate systems using model-data fusion breakthroughs the team have pioneered over the past 15 years. These models essentially work by fusing available data and science-based predictive models using machine learning to deliver predictions with higher and well characterised accuracy. We are now undertaking the development of our core platform, including the development of critical predictive models, and are looking for a talented computational ecologist to join us to develop models.
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