Based in modern offices in Guildford, this scientific engineering company develops state-of-the-art modelling and optimisation software used in geological industries. Their tools enable critical decision-making and simplify highly complex scenarios via solutions integration.
To keep the company at the cutting edge of their industry, they are looking to expand their innovation team and seek Research Software Scientists / Engineers to join them. The team explore new software-based technologies and methods, turning cutting-edge ideas into real world product features with practical value. Key areas of interest are applied algorithms for model development, AI / ML, optimisation, digital twins, agent-based and probabilistic systems and advanced computing.
This role would suit someone with a research mindset, who would not be fazed by uncertainty, and who would enjoy the exploratory aspects of shaping technical direction.
Requirements
1. PhD (or Master’s with additional relevant experience) in Engineering, Computer Science or Physical Sciences which has included demonstrable project experience in scientific programming using Python, C++ or C# (ideally all three).
2. Novel research experience in machine learning, optimisation or probabilistic modelling which should have included the development of new algorithms, use of numerical methods and computational modelling.
3. Any exposure to digital twins, agent-based systems, reinforcement learning or advanced optimisations methods would be desirable.
For senior candidates, additional experience in mentoring, project leadership on innovation-based projects, or strategic direction would be advantageous.
The role is fully onsite at their Guildford offices with opportunities for international travel. Compensation includes an excellent salary and wide range of benefits including health and dental insurance, enhanced pension contributions, and performance bonus. For qualifying candidates, relocation expenses are available.
Keywords: AI / ML, Optimisation, Python, C++, C#, Numerical Methods, Algorithms, Modelling, Geological Engineering, Onsite
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Recommend for £250