15 Postdoctoral Fellowship at Heriot-Watt University, Edinburgh, United Kingdom

A Post-Doctoral Research Associate (PDRA) is needed for a research project, fully funded by the Engineering and Physical Sciences Research Council (EPSRC), initially for 12 months from 1st July 2023 or as soon as possible but can be extended depending on the availability of funding. 

The research project COG-MHEAR: Towards cognitively inspired 5G-IoT enabled, multi-modal Hearing Aids funded by UK research council (EPSRC), involves six academic partners including University of Edinburgh and industrial partners including Bell-Labs, Alcatel-Lucent Technologies, USA and Sonova AG. The research will explore and validate the new concept hearing-aid to transform hearing care by 2050, we aim to completely re-think the way HAs are designed. Our transformative approach – for the first time – draws on the cognitive principles of normal hearing. Listeners naturally combine information from both their ears and eyes: we use our eyes to help us hear. 

We will create “multi-modal” aids which not only amplify sounds but contextually use simultaneously collected information from a range of sensors to improve speech intelligibility. Adding in these new sensors and the processing that is required to make sense of the data produced will place a significant additional power and miniaturization burden on the HA device. We will need to make our sophisticated visual and sound processing algorithms operate with minimum power and minimum delay, and will achieve this by making dedicated hardware implementations, accelerating the key processing steps. 

The PDRA will work as part of the Prosperity Partnership between Heriot-Watt University, the University of Edinburgh and Leonardo UK. This project aims to address the current limitations of traditional frame-based sensors and associated processing pipelines with a new family of algorithmic architectures that mimic more closely the behaviours of biological brains. 

Spiking neural networks (SNNs) can offer increased processing speed and reduced power consumption, especially when implemented on dedicated hardware (neuromorphic chips or FPGAs). Standard SNNs are typically fed with spiking data (e.g., streams of binary values), and the output of each layer remains spiking. They are particularly well suited for several new sensors, such as event-cameras, or single-photon detectors (used for single-photon Lidar), that natively produce event-like data that is compatible with spiking networks. Similarly, detection events from RF (radar and electronic surveillance) sensors can also be seen as temporal events. While data from current sensors can be manually converted into events for fast processing, it is also possible to develop hybrid structures where some layers (such as the input and/or output layer) are traditional (continuous) layers. 

SNNs appear promising at the conceptual level but they currently suffer from limitations preventing the broad deployment. First, efficient and scalable training procedure are still needed, irrespective of whether the training is done off-line on a traditional GPU-based architecture, on neuromorphic hardware. Second, deployment of SNNs on actual neuromorphic hardware can come with additional structural constraints on the SNNs, such that quantization of the network weights which induces additional constraints on the training strategies. 

High-resolution spectroscopy is vital to optical astronomy, and for decades, astronomers have used atomic emissions from hollow-cathode lamps to calibrate the spectrographs in optical telescopes. The limitations of such light sources mean that the community is now turning to astrocombs, which provide a sequence of ultra-narrow, drift-free, regularly spaced optical frequencies on a selectable multi-GHz grid. Uniquely, astrocombs must exhibit broad spectral coverage and wide inter-mode spacings, a technically demanding requirement that has limited the number of on-sky installations 

In this context, Heriot-Watt’s Ultrafast Optics Group is seeking to recruit a full-time post-doctoral researcher from 1 November 2024 on a 36-month contract at Grade 7 to work on an STFC-funded project developing new concepts in astrocomb technologies and calibration.  Based at Heriot-Watt’s Edinburgh Campus, in the School of Engineering and Physical Sciences, the Ultrafast Optics Group is a diverse, multi-national research team developing ultrafast laser sources for spectroscopy, metrology, and astrophotonics.

Astronomers use high resolution spectrographs to record the pattern of colours received from distant objects, but the wavelength axes of these instruments can shift slightly over time, and so must be regularly calibrated with an external reference light source to ensure consistency between observations. The emerging light sources of choice are astrocombs, a laser technology giving a sequence of ultra-narrow, drift-free, regularly spaced optical frequencies on a selectable grid spacing of 5-50 GHz. When referenced to GPS time, astrocombs provide atomically traceable optical frequencies with near-Hz-level precision.  Current astrocombs are complex and expensive, and their limited reliability requires regular maintenance.  Work in Heriot-Watt’s Ultrafast Optics Group seeks to address these issues by developing a new, simple and robust astrocomb platform based on diode-pumped Ti:sapphire laser. We are seeking to recruit a full-time post-doctoral researcher from 1 May 2024 on a 24-month contract at Grade 7 to work on an STFC-funded project to develop such lasers. 

Based at Heriot-Watt’s Edinburgh Campus, in the School of Engineering and Physical Sciences, the Ultrafast Optics Group is a diverse, multi-national research team developing ultrafast laser sources for spectroscopy, metrology, and astrophotonics. 

The successful candidate will be part of the Ocean Carbon-And-Nutrient-Dynamics (Ocean-CANDY) research group at the Lyell Centre, working on the Horizon Europe and UKRI (Innovate UK) funded project ‘Ocean-ICU’ (Ocean-Improving Carbon Understanding; https://ocean-icu.eu). The Ocean-ICU project aims to produce new data, information and understanding of the role of the ocean in the global carbon cycle. This position is in support of two Ocean-ICU work packages: WP2, Current state of the ocean carbon cycle; and WP3, Impact of climate stressors on the Biological Carbon Pump. The work planned is a collaboration between LSCE (Laboratoire des Sciences du Climat et de l’Environment, Paris) and the University of Exeter (Exeter Marine and Atmospheric Science group), and will involve a number of extended visits to these institutions. 

The candidate will explore how biogeochemical model parameterizations of pelagic calcium carbonate producing organisms in terms of biodiversity and dissolution of their carbonate tests influences ocean alkalinity. The candidate will parameterize different types of pelagic calcifiers and diversity of their dissolution rates in the NEMO-PISCES (Nucleus for European Modelling of the Ocean, Pelagic Interaction Scheme for Carbon and Ecosystem Studies) in order to test how modeled alkalinity reconstructions can better match alkalinity observations (e.g., from the Global Ocean Data Analysis Project). Exploring dissolution rates of different pelagic calcifiers will help to highlight how biology impacts the Carbonate Counter Pump, as well as identify potential abiotic or biotic mechanisms impacting alkalinity profiles in the ocean. 

The School of Energy, Geoscience, Infrastructure and Society is one of the UK’s leading institutions for multidisciplinary research and teaching in areas critical to economic development and societal equity. We aim to attain excellence in the teaching of the core disciplines that contribute to a sustainable, resource and energy efficient society and are committed to undertaking high value, high impact research and knowledge transfer in topics of relevance to global challenges.

The School enjoys facilities consistent with its location in modern, purpose-built accommodation on the Edinburgh campus. The School is well equipped in respect of laboratories for materials, structures, engineering, building services and hydraulics research and teaching.  Our research and teaching facilities in Dubai and Malaysia are contained within purpose-built campuses in Dubai Academic City and Putrajaya, respectively. 

The post will be in the Interaction Lab at Heriot-Watt University’s School of Mathematics and Computer Science (MACS), see http://www.macs.hw.ac.uk/InteractionLab. The successful candidate will join a group of about 20 people, consisting of postdoctoral and PhD researchers and faculty with expertise in NLP, NLG, Ethics in AI, Reasoning, Generative AI, Multimodal and Embodied AI, machine learning, and conversational interfaces. The project affiliated with this position is part of a successful series of projects in Ethics in NLP and involves the close collaboration with 6 members with an expertise in NLP, NLG, Ethics, Multimodality, Generative AI, and Psychology. It also involves collaborations with the University of Edinburgh (main collaborator; Social Science, and Education), as well as numerous charities focused on Gender-Based Violence including “EmilyTest”,  and “End Violence Against Women”. We also have established and ongoing collaborations with the Alan Turing Institute , Bocconi University Milan, Fondazione Bruno Kessler, University of Turin, Amazon Science, and Google Research.[IK1]  We also competed in the Amazon Alexa Challenge 2017 and 2018, on conversational AI, having reached the final on both occasions, and competed in the Simbot Alexa Challenge in 2022/3.  We have also recently spun-out a conversational AI company, Alana AI (see www.alanaai.com).

The advertised position will be part of the “Equally Safe Online” EPSRC-funded project (EP/W025493/1). The focus is on Ethics In AI, NLP, and Generative AI methods for identifying, and mitigating instances of online gender-based violence (GBV). To achieve this, we will build on our success in the EPSRC project “Designing Conversational Assistants to Reduce Gender Bias” (EP/T023767/1).  In contrast to previous approaches to address GBV and other similar societal issues, we follow a co-creating approach with experts and stakeholders in the field (aka social scientists, 3^rd sector charities on GBV). In other words, we do not only focus on *how* to automatically detect online abuse, but also re-think *what* it is we need to detect, how we can *support* the victims and how to *prevent* online GBV through promoting digital citizenship (i.e. prevention and intervention aimed at perpetrators and bystanders). 

The appointed individual will work on the development of technologies for non-mechanical beam steering, developing systems integrating complex 3D waveguide circuits fabricated using ultrafast laser microfabrication techniques. The post will also explore the testing of complete non-mechanical beam steering systems once these systems reach an appropriate stage of development. 

We are seeking an excellent, creative, and motivated postdoctoral research associate to work with us on the techno-economic analysis of a range of greenhouse gas mitigation projects. The two projects are: 

ConsenCUS, an H2020 funded CO₂ capture and utilisation Project. This major EU project aims to develop and pilot innovative electrolysis-based CO₂ capture and conversion technologies that will be scaled up and tested at three industrial sites across Europe, bringing the technology to TRL7. The technology development will be complemented by extensive supply chain and network optimisation, process model development and simulation, and techno-economic, life cycle, and societal analysis. The PDRA will work internationally with project partners to: 1) perform simulations to steer plant design & integration with natural gas-fired combined heat and power plants; 4) undertake techno-economic analysis of the capture and conversion technologies to identify cost-optimum solutions. 

The Quantum Photonics Laboratory seeks a talented scientist to undertake research on the development of a fully autonomous pilot-line for van der Waals (vdW) heterostructure device manufacturing. Significantly, the goal of the project is to achieve high-quality, reproducible, and deterministic layer-by-layer assembly of so-called moiré materials, with homogeneity at the quantum scale. The project will combine robotic control of all assembly steps with efficient computer-vision, machine intelligence algorithms, and the in-situ metrology techniques. 

The research associate will focus in-depth. We will develop and integrate the tools into a complete state-of-the-art autonomous 2D manufacturing system with a user-friendly interface. The projects will be conducted within the framework of a European Research Council Advanced Grant which aims to drive forward programmable moiré materials with quantum-level homogeneity and reproducibility. We will particularly target devices which offer reproducible physical effects including emergent phases of strongly correlated matter that are extremely sensitive to quantum interactions and hence device homogeneity. A significant goal is to demonstrate that we can engineer and probe strongly correlated states and directly link the structural properties to the emergent phase diagrams.  

The Post Doctoral Research Associate (PDRA) will work on the project “Complete Additive Manufacturing of a Laser Chassis” which is part of the “Making Challenge” of the “Smart Products Made Smarter” (EPSRC funded EP/X025365/1) Prosperity Partnership between Heriot-Watt University, University of Edinburgh, and Leonardo UK. 

This major project of the partnership is focused on the design, manufacture, and test of a future industrial laser chassis, utilising powder-bed laser fusion metal additive manufacturing (AM) and advanced laser-based manufacturing. The project is split into three areas: 1. Optical, mechanical, and thermal design of a compact and robust laser, exploiting the existing capabilities of additive manufacturing; 2. Testing and analysing new concepts on metal 3D printed parts; 3. 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.

The project is in close collaboration with Leonardo engineers, who will jointly execute some of the project tasks.  

The successful candidate will be required to rapidly embrace the body of knowledge vested in the research teams and will grasp the technical challenges of designing and developing a complete additively manufactured laser chassis. 

The Post Doctoral Research Associate (PDRA) will work on the project “Robotic Assembly and Maintenance” which is part of the “Manipulation Challenge” of the “Smart Products Made Smarter” (EPSRC funded EP/X025365/1) Prosperity Partnership between Heriot-Watt University, University of Edinburgh, and Leonardo UK. 

Laser systems are typically highly engineered and complex with tens of components required to be positioned to sub-micron accuracy. Although significant resource is spent in tolerancing and assembly engineering there is still a requirement for the exact positioning of optical components to be aligned, most often through an active alignment process by skilled engineers. 

This major project of the partnership is focused on automating the alignment process for these complex systems. Drawing upon the results and expertise developed within a major three year interdisciplinary UK Government funded project (EPSRC funded EP/V054497/1 “Developing Machine Learning-empowered Responsive Manufacture Of Industrial Laser Systems”) this project aims to create and demonstrating a robust, flexible, industrial system capable of carrying out alignment tasks autonomously. 

The project is split into four areas: 1. Automated alignment strategies and autonomous system control; 2. Laser systems design for automation; 3. Digital twining and manufactured systems view; 4. Robotic and mechatronic systems. 

The project is in close collaboration with engineers from Leonardo and the National Robotarium (based at Heriot-Watt University), who will jointly execute some of the project tasks.