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PhD Studentship: Classic Doctoral Training Grant Funding Information

Birmingham City University

Qualification Type: PhD
Location: Birmingham
Funding for: UK Students
Funding amount: A tax-free stipend of £15,285 per annum
Hours: Full Time
Placed On: 21st December 2021
Closes: 31st January 2022
Reference: PAB-PhD

This 36 month (3 year) fully-funded PhD Studentship, in-line with the Research Council values, comprises a tax-free stipend of £15,285 per annum (paid monthly) and FT Home fees (£4,500 for 2021-22). The funding is renewable annually for up to 36 months in total, subject to satisfactory progression within your PhD research.

This opportunity is open to UK applicants only.

Please apply by clicking on this link.

Project 1: Permissions Agnostic Behaviour Tracking for Mobile Applications (PAB) 

Project reference: PAB-PhD 

Contact: atif.azad@bcu.ac.uk

  • Topics of Interest 
    Artificial Intelligence (AI), Machine Learning, Mobile App Environment, Edge Computing, Behaviour Tracking, Personalisation, Federated Learning and Ethical AI.

Background:  

Mobile devices are ubiquitous in the modern era: approximately 90% of UK adults own a smartphone. Therefore, mobile advertising has grown rapidly over the last decade, now accounting for more than half of all digital ad spend in the UK. One driver for this growth has been the user tracking properties of connected smart devices, which facilitates the behavioural targeting of users. Such tracking, whilst effective, has frequently raised privacy concerns. These concerns have begun to manifest in regulations such as the General Data Protection Regulations (GDPR) and the California Consumer Privacy Act (CCPA), which restrict the tracking activities that advertising networks conduct without the explicit permission of the user. This has also led mobile operating systems - most notably, Android and iOS - to tighten the circumstances under which mobile app developers can request certain app permissions, particularly permissions relating to user location and activity.

Traditionally, these behavioural tracking networks have collected data from devices (and across devices), associated this data with a tracking ID, and performed large scale data mining off-device to infer a user's behaviour, lifestyle, preferences etc. These insights are then used to match users with advertising campaigns. This off-device data mining produces large caches of personally identifiable data that can be processed, resold and exploited without the original user's knowledge; these caches are also a prime target for cyber-criminals. It is likely that such activities will continue to be restricted, regulated and/or prohibited in the future, which is leading advertising businesses to look for ways to continue to perform ad-targeting, but with a greater emphasis on protecting the privacy of the end-user.

Challenge: Supported by industrial partners – Covatic - the project will use Machine Learning (ML) to segment the customer base and/or identify the change of a user's behavioural patterns while using minimal (privacy agnostic) information that is unlikely to go behind privacy walls erected and further elevated overtime by the mobile operating systems. For example, a challenge is to infer geographical whereabouts without using the user's location information.  

PhD Applicant Profile

Essential:

  • A strong background in AI/ML methods.
  • MSc/bachelor's in computer science/AI/Data Science and industrial/open-source projects or publications; Or a bachelors in a STEM discipline but over 5 years of industrial experience in Data Science and AI. 
  • Experience in scientific programming: Python with Pandas, Numpy etc (or suitable experience in R/Julia/C/C++ etc)
  • A team player (showcased with STAR technique). 

Desirable:

  • publications in high impact factor journals and A/A* (core-ranking) rated conferences; 
  • substantial industrial experience in Data Science and Artificial Intelligence. 
  • experience in mobile development (Android or iOS) and MongoDB - further training will be available; 

The deadline to apply is 23:59 on 31st January 2022.

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