PhD Studentship - Opportunistic constraint-aware continual machine learning

The undertaken research work is part of an EU project called ExtremeXP (Experiment driven and user experience-oriented analytics for extremely precise outcomes and decisions) under the direction of Prof Hamid Bouchachia. The project will develop scenario-driven and opportunistic machine learning with the aim to (1) design and develop AutoML mechanisms for performing scenario-based algorithm and model selection considering on-demand user-provided constraints (data, performance, resources, time, model options), (2) devise mechanisms for continual learning in algorithm and model selection: as new data sets become available, the so-far learned selection strategies are continuously adapted.

We are seeking to fill the position with a talented and enthusiastic candidate with excellent analytical, programming, communication and scientific writing skills, who will contribute to the delivery of the project (planning, designing and conducting the proposed research and producing published outputs). Candidates must have strong analytical background in Machine Learning preferably with good exposure to Meta-learning, Continual Learning, AutoML, Scalable ML and Trustworthy AI. The project will target new and original ML algorithms and strategies leading to exploitable constraint-aware ML and analytics services.

This is a fully-funded PhD studentship which includes a stipend of £17,668 each year to support your living costs. 

This project is conditional upon the funding being secured. 

Key information

Next start date:

24 April 2023

Location:

Bournemouth University, Talbot Campus

Duration:

36 months

Entry requirements:

Outstanding academic potential as measured normally by either a 1st class honours degree or equivalent Grade Point Average (GPA), or a Master’s degree with distinction or equivalent. If English is not your first language you'll need IELTS (Academic) score of 6.5 minimum (with a minimum 6.0 in each component, or equivalent). For more information check out our full entry requirements