Reinforcement and Machine Learning Models for Coordinating Heterogeneous Hardware and Software Platforms in IoT Environments

Primary supervisor

Additional supervisors

  • Christos Kotselidis
  • Sarah Clinch

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Other projects with the same supervisor

Funding

  • Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

Project description

Building complex and multi-component systems for the Internet-of-Things requires the materialization of systems requirements over heterogeneous hardware, software and network environments. The orchestration and configuration of a system which prioritizes the requirements specified by the users (for example covering the dimensions of energy efficiency, resiliency and application latency) under the constraints imposed by the available hardware, software and network settings, defines a complex decision problem. Underlying this decision problem resides the intrinsic complexity of interpreting the impact of each design element of the stack into the satisfaction of the user requirement. This complexity is defined by the size of the configuration space entailed by the set of possible design strategies, from the hardware to the application level.

This PhD project explores the use of reinforcement learning, machine learning and semantic representation models as the foundation to approach the multi-criteria optimisation problem underlying finding an optimal configuration for IoT systems operating in heterogeneous settings. The project is intrinsically multi-disciplinary and will cover all the dimensions involved in delivering an efficient end-to-end IoT system (from the chip level through software to the end user). The project will explore contemporary architectures and techniques in machine learning (targeting efficient representations for one/zero-shot learning and transfer learning) and their interplay with reinforcement learning.

Applicants are expected to have:

* An excellent undergraduate degree in Computer Science or Mathematics (or related discipline), and preferably, a relevant M.Sc. degree.
* Confidence and independence in programming complex systems in Java or Python. Industry experience is a plus.
* Previous academic or industry experience in Machine Learning (desired).
* Excellent report writing and presentation skills.

Please note that applicants must additionally satisfy the standard requirements for postgraduate studies at the University of Manchester, such as a first-class or high upper-second class (or an equivalent international qualification) and English language qualifications, as stated in the PGR guidelines.

Qualified applicants are strongly encouraged to informally contact Andre Freitas, (andre.freitas@manchester.ac.uk), Christos Kotselidis (christos.kotselidis@manchester.ac.uk) and Sarah Clinch (sarah.clinch@manchester.ac.uk) to discuss the application prior to applying.

Person specification

For information

Essential

Applicants will be required to evidence the following skills and qualifications.

  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.

Desirable

Applicants will be required to evidence the following skills and qualifications.

  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
  • You will have good time management.

General

Applicants will be required to address the following.

  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Why do you believe you are suitable for doing Postgraduate Research?
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