Research Associate in Signal Processing for Advanced Manufacturing Systems
Salary: Grade 7 £31,302 - £39,609 per annum.
Hours: Full time
Closing Date: 10th May 2019
Contract Type: Fixed term for a duration of 24 months
We have an opportunity for a researcher with expertise in signal processing, system identification and/or control engineering to undertake an ambitious programme of research, supported by EPSRC as well as EU Horizon 2020. The overall aim is to create an autonomous system for capturing and quantifying process knowledge via the use of data-driven machine learning models, and subsequently make use of such information to monitor and optimise a complex manufacturing processes (metal 3D printing for aerospace, automotive and civil engineering parts).
You will focus on developing mathematical and computational algorithms to optimise parts tailored for production via direct energy deposition additive manufacturing processes, and programming these in software libraries compatible with the adopted tools and digital workflow. A key aim is to develop novel statistical signal processing and data analytics algorithms for modelling and monitoring of additive manufacturing processes and programming these in software libraries. A key aim is to develop a pipeline of data analytics algorithms for a variety of data from the different stages of additive manufacturing processes. The datasets can range from being spatio-temporal and time-series to high dimensional multisensor data.
This position provides significant training opportunities. Given the multidisciplinary nature of the project, and planned
research visits, you will have opportunities to learn new skills (technical and professional) by closely collaborating with
partners from academia and industry. You will also have the opportunity to participate in professional skills training for
researchers and leaders, provided by the host university. You should have a good relevant honours degree in engineering/computer science/machine learning /applied mathematics (or equivalent experience) and hold (or be close to completion) a PhD in signal processing, system identification, control engineering, machine learning, computer science or a related area (or equivalent experience). You should also have in-depth knowledge of statistical signal processing, including aspects of modelling and estimation, fault detection and/or model-based process monitoring, in particular those relevant to extracting information from data. Strong software development skills (for example in MATLAB, Python, C++), and the ability to plan work effectively to project timescales and produce project
deliverables on time are essential.