Job opportunity
Postdoctoral Research Associate in Laser Processing of Additively Manufactured Parts (Deadline 21 February 2025).
The Post Doctoral Research Associate (PDRA) will work on developing and demonstrating laser-based processing of additively manufactured parts, specifically laser-based polishing of AM parts and ultra-fast-laser-welding of optical laser components to AM parts.
For more information and to apply
PhD opportunities
These PhD projects forms part of the Prosperity Partnership Programme, Smart Products Made Smarter.
We are pleased to invite applications for PhD studentships to work as part of a leading team of experts. These studentships will be supported by an enhanced stipend of £21,400 per year over 3.5 years.
This grant, sponsored by the EPSRC, is a collaboration between academia and Leonardo. There are currently PhD opportunities available to work on diverse topics as part of this collaborative team. The work will involve strong links with industry.
The research addresses a broad range of challenges. These challenges exemplify future product lifecycle management from smart concept, design, development and manufacture to enhanced end-user capability, united by a common digital thread to enable smarter products to be made smarter. Each challenge area has clearly identified initial research themes and associated research challenges to be addressed.
It is advised to discuss the PhD research with the relevant supervisors before application.
The Manipulation Challenge
PhD project topic – Smart Factory
The advent of Industry 4.0 has ushered in a new era of manufacturing, marked by the integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and digital twins. This research project will investigate the methodologies and technologies necessary to create an accurate and dynamic digital twin that reflects the real-time status of a manufacturing process, thereby enabling enhanced decision-making, predictive maintenance, and improved production efficiency.
This project will be jointly supervised by:
Prof Prof Jonathan Corney j.r.corney@ed.ac.uk
Dr Matjaz Vidmar matjaz.vidmar@ed.ac.uk
Cross-cutting between the Making challenge and the Manipulation challenge
PhD topic – Laser Based Manufacturing of Precision Optical Systems
Manufacturing of lasers and similar high precision optical systems is challenging in terms of the very high precision and thermo-mechanical stability that is required whilst providing for active alignment processes. These challenges are particularly acute in applications such as defence in which production volumes are moderate and the ultimate environmental operating conditions can be very harsh. This results in very costly manufacturing and testing approaches. PhD Research is hence required into manufacturing processes, materials and designs that can reduce cost and the need for rework whilst improving mechanical stability. Projects will focus on the opportunities offered by laser-based manufacturing processes such as metal additive manufacturing, laser surface structuring and laser bonding of highly dissimilar materials including novel materials, geometries and processes.
This project will be supervised by:
Prof Daniel Esser m.j.d.esser@hw.ac.uk
For more information and to apply
The Computation Challenge
PhD project topic – Data-Driven Computational Sensing and Imaging
Today’s state-of-the-art imaging and sensing rely as much on computation as they do on sensor hardware. Furthermore, computational sensing and imaging is increasingly exploiting data-driven and machine learning solutions to enhance performance and develop novel hardware/software co-designed sensing systems. However, in defence scenarios it is vital that verifiable algorithmic solutions are used, which places restrictions on which machine learning approaches are admissible. Importantly, fully black box machine learning solutions should be avoided. This project will therefore focus on the development of novel algorithmic and mathematical frameworks to exploit data and machine learning for imaging and sensing within a controlled explainable and verifiable manner. There will be a specific focus on RF and electro-optic/IR sensor modalities.
This project will consider a range of algorithmic and machine learning technologies including: low rank models and/or auto-encoder type architectures to identify low dimensional data representations; physics-informed and physics aware neural networks that ensure the machine learning solutions adhere to necessary physics within the sensing problem; machine learning solutions targeted reducing computation or processing time; robustness to noise, outliers and adversarial attacks; and Bayesian and variational architectures that can provide uncertainty quantification.
This project will be jointly supervised by:
Prof Mike Davies, Mike.Davies@ed.ac.uk
Prof James Hopgood, James.Hopgood@ed.ac.uk
PhD project topic – Sensor Fusion and management in Autonomous Airborne Platforms
Sensor networks, sensor fusion and management techniques address key challenges in intelligence, surveillance, target acquisition, and reconnaissance (ISTAR). Opportunities in adaptive data-driven sensor tasking and resource management include adaptive sensor placement, adaptive waveform design to reflect the target reflection characteristics and channel environments, and adaptive sensor selection. Although these problems have solutions in specific use cases, this theme will consider scenarios with broader applications involving multiple heterogeneous sensors on single or multiple cooperative autonomous airborne platforms.
The solutions developed in this should be robust to dynamic and congested environments, adverse weather conditions, and mutual sensor interference. A range of algorithmic and signal processing or machine learning technologies will be considered, as well as specific technical challenges. For example, projects in this theme will consider aspects related to wide area motion imaging (WAMI), position, navigation, and timing issues (PNT); robustness to adversarial attack; sensor fusion and tracking applications; use of kernel and Monte Carlo methods; outlier-robust (and other metrics) messages in belief propagation algorithms; and scheduling in large dynamic networks.
Probabilistic and Bayesian frameworks will be preferred to enable uncertainty quantification and management.
The techniques, solutions, and challenges proposed in this theme has applications across a range of defence and civilian applications, including search and rescue, law enforcement, and remote sensing.
This project will be jointly supervised by:
Prof James Hopgood, James.Hopgood@ed.ac.uk
Prof Mike Davies, Mike.Davies@ed.ac.uk
PhD topic – Compressed/low SWAP-C sensing
While most sensing and processing tasks have been traditionally optimized separately, joint development of sensors, computing hardware and algorithms, designed to achieve specific tasks can lead to lower size, weight, power and cost (SWaP-C) systems, without significant degradation of the information recovered. A typical example is compressed sensing, achieving data compression via linear transformations. Although non-linear dimensionality reduction/compression can further reduce data volumes, understanding the trade-offs of such methods while ensuring performance guarantees remains challenging and crucial, especially in a defence and security context. This theme will focus on the development of novel statistical methods for low SWaP-C sensing, targeting existing and next-generation resource constraints hardware. This theme finds direct application in electro-optics (EO) sensing and fusion of heterogeneous sensors.
This theme will consider a range of methodological tools from user-defined to data-driven compression schemes, enabling identification of compact representations from exemplar data. Probabilistic and Bayesian frameworks will be preferred to enable uncertainty quantification and management, as well as simplification of acquisition/processing pipelines. Of particular interest will be algorithms compatible with implementation on fixed-point hardware and neuromorphic processors (spiking architectures).
This project will be supervised by:
Prof. Yoann Altmann y.altmann@hw.ac.uk