Machine Learning in Dynamical Systems for Sensor Signal Processing

Dynamical system models have been the main pillar of conventional model-based approaches in control, signal processing and sensor fusion: Sensor signal processing and inference algorithms for applications such as multi-object detection and tracking, robotic simultaneous localisation and tracking (SLAM) and calibration of autonomous networked sensors are designed by combining the known physics and stochastic elements into dynamic system models. Model inaccuracies can be mitigated to achieve significant performance gains in inference and decision-making by leveraging data and model size, following the recent advances in machine learning. However, learning from data in dynamical system models to jointly address the epistemic and aleatoric uncertainties involved remains a challenge stemming from a range of factors such as noisy data and labelling, inhomogeneous sampling, model complexity and the intractability of posterior inference. 

This PhD project aims to conduct underpinning research to address problems involving the learning of hierarchical, time-varying multi-dimensional state space models for dynamic objects/phenomena, noisy measurements made in complex backgrounds and/or in the presence of calibration errors.

The incumbent will have the opportunity to steer the direction of the research in consideration of the impact on engineering problems, including learning models for complex backgrounds in radar detection, learning of birth and trajectory models to improve detection and tracking, or semi-supervised/unsupervised training of sensor data classifiers.

The application documents should provide sufficient information and evidence regarding the applicant’s background and goals, and include:

• A Personal Statement
• CV
• Official certificates and transcripts (and English language translations, if applicable)
• English Language certificate (if applicable, for more information, please see here: https://study.ed.ac.uk/programmes/postgraduate-research/947-engineering)
• 2 Recommendation Letters
• Research proposal (optional for candidates who are not self-funded or applying for scholarships. Must be related to the project description) 

Dr M Uney is an experienced scientist with interests in signal processing, machine learning, probabilistic models and Bayesian computations in sensor fusion and signal & information processing.

Prof Mike Davies holds the Jeffrey Collins Chair in Signal and Image Processing at the University of Edinburgh. His research interests are in machine imaging, data-driven computational sensing and imaging, and sensor and information fusion.

This position is open to UK/EU nationals and international applicants, and offers an annual stipend of £21,935 (as of 2024-25 fiscal year, subject to revision) for a duration of 3.5 years with £5000 research expense funds for the duration of the study.      

UK nationals and other eligible applicants might choose to align this studentship with the Sensing, Processing and AI for Defence and Security Centre for Doctoral Training (SPADS CDT) at the University of Edinburgh’s School of Engineering, and benefit from an enhanced stipend (subject to CDT administration approval and security clearance, see the eligibility criteria in the link) upon acceptance. Eligible applicants should state their interest in their Personal Statement.

Minimum criteria:

- A 2:1 Master’s Degree in engineering, computer science, statistics, physics or in an appropriate subject or a related discipline.

- The University’s English language requirements  

Preferable criteria:

-    A 1st class Undergraduate Degree in engineering, computer science, statistics, physics or in an appropriate subject or a related discipline.

-    A 1st class Master’s Degree in engineering, computer science, statistics, physics or in an appropriate subject or a related discipline.

-    Experience in scholarly writing.

Tuition fees + stipend are available for Home/EU and International students

Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere

Further information and other funding options.