We are recruiting a mathematical modeller for emmulation of physical systems with Gaussian process models. We seek creative researchers who: (1) are passionate about their work, (2) are prepared to learn new skills and (3) can collaborate closely with teams of engineers implementing state of the art algorithms.
The job will involve developing and deploying machine learning algorithms for the modelling and analysis of data, with a particular focus on physical systems. Challenges involve design constraints that may arise from, for example, hardware implementation or latency requirements.
- Undergraduate degree in computer science, software engineering or undergraduate degree in numerical discipline (e.g. physics, maths, engineering).
- Industrial experience in physical systems modelling.
- Experience in Python, Matlab or R
- Hands on experience in predictive modelling and analysis
Masters qualification in machine learning, statistics or in another highly quantitative/analytical field
Recent record of publication in internationally-leading machine learning or statistics venues (e.g. NIPS, ICML, AISTATS, UAI, JMLR, TPAMI, JASA, JRSS, Annals of Statistics).
Hands on experience in software systems development
Experience with one or more of C++, Java, Scala, C#