Start Date
Immediate
Expiry Date
01 Feb, 25
Salary
46485.0
Posted On
01 Nov, 24
Experience
0 year(s) or above
Remote Job
No
Telecommute
No
Sponsor Visa
No
Skills
Good communication skills
Industry
Information Technology/IT
Directorate: School of Engineering & Physical Sciences
Salary: Grade 7 (£36,924-£46,485)
Contract Type: Full Time (1FTE), Fixed Term (18 Months)
Rewards and Benefits: 33 days annual leave, plus 9 buildings closed days for all full time staff (Part time workers should pro rata this by their FTE). Use our total rewards calculator: https://www.hw.ac.uk/about/work/total-rewards-calculator.htm to see the value of benefits provided by Heriot-Watt University
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: