Please find below details for a paid summer placement. This placement is open to EPSRC-funded students.
If you are interested in applying for this opportunity, please let me know as soon as possible. Deadline for applications – 30 April.
Dates of placement
August – September/remote working (flexible)
Senseye is an exciting and rapidly expanding start-up in the field of condition monitoring and prognostics. The company develops a cloud-based product that integrates sensor data from manufacturing equipment and uses advanced machine learning techniques to identify problems before they happen and predict when machines are likely to fail. This helps engineers to focus their attention on machines that need it and to make better decisions. It helps manufacturing plants run more efficiently and reduces waste.
Characterising anomalous behaviour in time series
In this project you will develop techniques to identify and characterise anomalous behaviour in time series.
Senseye collects data from sensors attached to industrial equipment and for each machine there are multiple time series representing different physical quantities such as current, torque and pressure. We use this data to model the underlying state of each machine and to identify anomalous behaviour that may indicate a change in that state (and thus, for example, indicate whether a machine is about to fail).
We search for features such as spikes, step-changes and systematic changes in baseline. The aim of this project is generalise this search so that we can identify and classify features with arbitrary shapes on arbitrary timescales. These features can then be used as inputs to machine learning models.
This project is a mix of computing and theoretical work and will provide an excellent grounding in time series analysis, a set of analytical techniques that has widespread applications in science and industry.
Strong computing skills (Python experience highly advantageous).
Tel: 01483 683758
Mobile and answer phone: 07730 762 476