News announcement

The topic is "Bayesian Learning for Object Recognition from Noisy Time Series Data", see the advert for further details.

【招聘信息】

The overall objective is to introduce a generic Bayesian framework for robust sequential target discrimination from noisy time-series data. Drone recognition from radar data will be a key application area, for instance for unmanned air-traffic management. This research will leverage recent advances in sequential learning from time-series data (eg with kernel-based methods) and can involve incorporating any known priors on the features evolution over-time or the present noise or clutter using suitable (possibly hierarchical) stochastic state-space models. The key challenge is mitigating fluctuating and unreliable target (eg drone) classification results from supervised or unsupervised learning methods since: a) classifier uses features (eg kinematics or Doppler related) extracted from the sensor (eg radar) noisy time-series data, b) data and features quality significantly fluctuates over time (eg due to clutter, multipath, occlusions, etc) for a given target, c) some key salient features (eg related to micro-Doppler effects) are intermittently available.

【具体要求】

The research will be conducted in close collaboration with Aveillant-Thales, who will offer guidance and potentially real radar data for various targets of interest to validate the developed recognition algorithms. This also includes the applicant spending at least 3 months at Aveillant's offices in Cambridge.

Applicants should have (by the start date) at least a good 2.1 degree in information engineering, mathematics or computer science. A background in statistical signal processing and/or machine learning is highly desirable, with experience in one or more of the following: tracking and SMC methods, supervised learning for classification and deep learning, anomaly detection and pattern-of-life inference. Applicants are expected to have a solid programming/modelling skills, for example in MATLAB/Python/C++.

【奖学金介绍】

EPSRC DTP studentships are fully-funded (fees and maintenance) for eligible UK students. International (including EU) students will be considered for partial funding (home-level fees and maintenance) from the DTP.

Further details about eligibility and funding can be found at: 

https://epsrc.ukri.org/skills/students/help/eligibility/

https://www.postgraduate.study.cam.ac.uk/finance/fees

https://www.cambridgetrust.org/scholarships/

【申请方式】

Applications should be submitted via the University of Cambridge Applicant Portal www.graduate.study.cam.ac.uk/courses/directory/egegpdpeg, with Professor Simon Godsill identified as the potential supervisor. 

Applicants are highly encouraged to contact Dr Bashar Ahmad bia23@cam.ac.uk at the earliest opportunity to express their intention to apply with a two page CV (and other supporting documents, if any) or if they have any informal enquiries.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.