Overview
Your role is to lead research that contributes to our groundbreaking technology demonstrators, ensuring that Improbable’s decision support tools, training systems and digital twins embody robust and cuttingedge science.
The Research team brings together experienced researchers and software engineers from multiple disciplines interested in improving the performance, validity, richness and effectiveness of simulations of complex systems.
We regularly publish, and work closely with both industry and academic collaborators
this year, two of our researchers received Royal Society awards for their collaboration with leading academics from the Turing Institute, and Leeds, Exeter and Warwick Universities.
Areas for ImpactBecome rapidly wellversed on the stateoftheart in relevant technical areas, such as agentbased models, evolutionary computing and machine learningPursue independent research towards our strategic objectives.
This requires translation of ambitious but often vague objectives into concrete research proposals, with a realistic assessment of the necessary resources and expected outcomesTake a lead role in driving the strategy and execution of Research at the team level
define directions, set goals, mentor junior researchers, and drive change to support its impact, and growthTest and demonstrate ideas effectively in code.
Be comfortable working in at least one highlevel language like Python, and to a high engineering standard when appropriateMaintain existing relationships and foster new links with academic and industrial research partnersWe’d like to hear from you if you identify with the following
PhD in statistics, machine learning, computer science or a simulationrelated fieldExtensive experience in scientific computing with experience developing novel tools and methodologies for scientific modellers that allow them to model complex sociotechnical systems efficiently, realistically and robustlyTrack record of high profile research as evidenced by publications, patents and/or conference talksScientific programming skills in, e.
g.
, Python, Julia, R or C++Experience with deep learning and/or probabilistic programming libraries in Python e.
g.
Pytorch, Pyro is desirableExcited to be able to deliver functionality for agentbased models that exceeds the stateoftheart, and have it used by the most discerning customers in the defence and security industryExperience with agentbased models or microsimulations in any domainNb
While we think the above experience could be important, we can’t predict the future and so we’re keen to hear from applicants that believe they have valuable experience.
If you identify with the team & mission, but not all of the suggestions, then please still apply#LITH1About UsImprobable is determined to foster an environment where people can do their best work and feel like they belong.
We believe a healthy culture, strong values and contribution from a diverse range of individuals will help us to achieve success.
We do not discriminate based on race, ethnicity, gender, ancestry, national origin, religion, sex, sexual orientation, gender identity, age disability, veteran status, genetic information, marital status or any other legally protected status.
Life at ImprobableDiversity, inclusion & belonging
Science
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