Directorate: School of Engineering & Physical Sciences
Salary: Grade 7 (£36,924-£46,485)
Contract Type: Full Time (1FTE), Fixed Term (18 Months)
Detailed Description
This project is part of the new UK Hub in Quantum Sensing Imaging and Timing (QuSIT), that stands as an international centre of excellence for research and innovation, providing thought leadership, coordination and translation to impact across the quantum landscape in the UK and beyond. QuSIT builds upon a decade of government, institutional and industry investment, unifying expertise from two existing internationally leading Hubs in QT Imaging (QuantIC) and Sensing and Timing, maintaining coherence within the UK landscape, bringing together industry, government, and the international community. This Hub is a crucial vector for the delivery of the National Quantum Strategy, enhancing UK prosperity and national security.
This position aims at addressing computational challenges associated with data acquisition and information extraction from complex sensors and sensor networks. Crucially, uncertainty management and quantification tools are need during the development of new imaging and sensing systems. With the rapid deployment of data-driven methods, repliable uncertainty quantification remains a big challenge that requires the development of principled, mathematical tools. Such tools need to be able to handle a variety of working environments (e.g., dynamic environments), input data (from traditional frame-based data to non-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing, imaging and timing applications.
In this project, we will:
1. Develop Bayesian deep learning methods for event-based data, including single-photon detections and neuromorphic camera data.
2. Investigate Bayesian graph neural networks for complex sensor networks such as those involved in brain imaging
3. Develop and test data-driven methods for image and video processing for microendoscopy.
Key Duties and Responsibilities
4. Develop suitable algorithmic methods for live and real-time analysis of synchronous and asynchronous data.
5. Write research reports and publications. Analyse and interpret the results of own research and generate original ideas bases on outcomes. Prepare proposals and applications to external bodies, e.g. for funding purposes. Use initiative and creativity to identify areas for research, develop new research methods and extend the research portfolio.
6. Build internal contacts and participate in internal networks for the exchange of information and to form relationships for future collaboration. Work with academic colleagues on areas of shared research interest and contribute to collaborative decision making. Join external networks to share information and identify potential sources of funds.
7. Provide guidance as required to support staff, research students and any other students who may be assisting with the research.
8. Contribute, under supervision, to the planning of research projects, including the development of new grant/contract proposals. Make internal and external contacts to develop knowledge and understanding and form relationships for future collaboration.We are looking for a creative and highly motivated researcher willing to work as part of a multidisciplinary team.The ideal candidate will have a strong theoretical understanding and an experimental background in one or more of the following fields: Statistical signal/image processing, deep learning, machine learning, neuromorphic computing
9. Good communication skills and an appropriate publication record are essential.
10. Solid knowledge of Python and C++ is essential.
11. General tasks will involve scientific research; analysis and interpretation of data; communication with other investigators involved in this collaborative project; preparation of scientific papers
12. The successful candidate will be expected to conduct and lead their own experiments whilst also supervising the activities of junior group members and PhD students
13. Responsibilities will also include assistance in the day-to-day maintenance of the experimental facilities, liaising with companies and external collaborators
14. The successful candidate is also expected to be involved in our outreach activities, with roles that can be tuned to the specific preferences of the candidate but will involve for example interviews, talks for the general public and preparation of experimental demonstrators.
Please note that this job description is not exhaustive, and the role holder may be required to undertake other relevant duties commensurate with the grading of the post and its general responsibilities. Activities may be subject to amendment over time as the role develops and/or priorities and requirements evolve.
Education, Qualifications and Experience
Essential Criteria
1. Possess and maintain sufficient breadth or depth of specialist discipline knowledge and or research methods and techniques to work in the area (statistical signal processing and data-driven methods).
2. A record of high quality publications, and evidence of contribution to the writing of these publications proportionate to opportunity.
3. Strong experience with Python and C++ applied to research in real-time data processing.
4. Ability to articulate research work, both in technical reports / papers and by oral presentation
5. Ability to formulate and progress work on their own initiative
Desirable Criteria
1. Evidence of ability to present work effectively in person, e.g. at conferences and seminars.
2. Experience in leading the writing of scientific papers.
3. Evidence of ability, subject to opportunity, to guide other researchers, e.g. PhD students and undergraduate project students.
4. Capability to be self-directed and think innovatively.
5. Energy and enthusiasm for the project.
About our Team
The School of Engineering & Physical Sciences has an international research reputation and close connection with the professional and industrial world of science, engineering and technology. Our research ranges from fundamental sciences through to engineering applications, all of which are supported by strong external funding. We have around 150 full-time academic staff who drive this research activity and are based in 5 research institutes: the Institute of Chemical Sciences, the Institute of Photonics & Quantum Sciences, the Institute of Mechanical, Process & Energy Engineering, the Institute of Sensors, Signals & Systems and the Institute of Biological Chemistry, BioPhysics & BioEngineering. REF2014 named Heriot-Watt in the top 25% of UK universities, with 82% of our research ranked as world-leading or internationally excellent. Heriot-Watt ranked 9th university in the UK and 1st in Scotland for research impact.
The School of Engineering & Physical Sciences has received the Bronze Award from the Athena SWAN Charter recognising excellence in championing employment of women in the fields of science and technology, engineering and mathematics.
In addition we deliver teaching across 6 undergraduate and post-graduate programmes: Chemistry; Physics; Electrical, Electronic & Computer Engineering; Chemical Engineering; Mechanical Engineering and Brewing & Distilling. 90% of our students were satisfied overall with their course in over half of the University's subject areas. At subject area level the School ranked in the top 10 UK universities for Physics and we were 3rd in Scotland for Chemistry and 2nd for Mechanical Engineering and 1st in Scotland for Chemical Engineering.
The Institute of Signals, Sensors and Systems (ISSS) is one of five Research Institutes forming the research infrastructure of the School of Engineering & Physical Sciences (EPS). With 40 academic members of staff spanning 10 nationalities and 4 fields of expertise, ISSS aims to offer the full portfolio of expertise in the fields of signal and image processing, novel manufacturing technologies, microsystems, microwave engineering, mobile communications systems and autonomous systems. Of particular interest to ISSS is the design, modelling, simulation, processing of information from and system integration of sensors. The Signal and Image Processing Laboratory (SIPLab) at Heriot-Watt specializes in the design of advanced data science techniques with applications ranging from robotics to imaging and communication, in a large variety of fields including defence, astronomy, art investigation, or medicine. Our research activities range from signal and image processing theory to application, and impact different areas of society. SIPLab is active in both traditional and emerging areas, and currently covers the following topics:
Signal and image processing theory
Statistical signal processing, non-stationary processes, Bayesian inference, signal models, sampling theory, sensing techniques, optimisation theory and algorithms, multi-modal data processing, high-performance computing, mathematical image analysis, geometric modelling, acoustic signal propagation, Monte Carlo simulation methods, decision theory, uncertainty quantification, machine learning.
Applications and areas of key innovation
Image analysis, computer graphics, autonomous and assisted driving, neuromorphic computing and sensing.