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PhD Studentship: Artificial Intelligence/Machine Learning for Embedded Systems

Overview

  • Qualification type: PhD
  • Subject area: Machine Learning, Artificial Intelligence, Embedded Systems
  • Location/Campus: College Lane campus, Hatfield, UK
  • Start date: 1 May 2023 or as soon as possible thereafter
  • Closing application date: 31 March 2024
  • Duration: Full Time -  3 years

Project outline

This is an exciting opportunity to undertake a 3-year PhD studentship to work on state-of-the-art research for Artificial Intelligence/Machine Learning on Embedded Systems. In this research, you are expected to develop novel methods for the implementation of cutting edge machine learning and deep learning algorithms on embedded hardware platforms.

You will conduct in-depth literature reviews and market analysis to identify current trends and challenges in this area of research. You will design, simulate, and optimize machine learning based methods for implementation on embedded hardware platforms like MCUs, DSP, FPGAs. You will collaborate with interdisciplinary teams, including engineers, data scientists, researchers, and industry partners, to validate and prototype innovative methodologies. You are expected to publish research findings in reputable journals and present results at conferences, contributing to patents and to the academic and scientific community.

Supervisors

  • Principal supervisor: Dr Iosif Mporas
  • Second supervisor: Dr Somayyeh Timarchi

Entry requirements

Essential

  • A first or upper second-class degree (or equivalent) in a relevant discipline such as, electronic engineering, computer science, maths, etc.
  • Knowledge in programming hardware boards (MCUs, DSP, FPGAs).
  • Knowledge in machine learning and deep learning.
  • Ability to implement specialised innovative ideas into code.
  • Ability to work collaboratively and manage time independently to meet deadlines.
  • Problem-solving skills and attention to detail.
  • Excellent oral and written English communication skills, including the ability to communicate with clarity on complex information.
  • Excellent IT literacy.
  • Applicants must be self-motivated.

Desirable

  • A relevant master’s degree will be an advantage.
  • Publications in high-impact international journals and conferences.
  • Experience in embedded hardware systems and VHDL.
  • Good knowledge of programming language Python preferably in Linux environment.
  • Previous experience with state-of-the-art AI/ML software libraries (TensorFlow, Scikit-Learn, PyTorch, etc).

Eligibility

The studentship is open to UK/EU and international applicants.

How to apply

Informal enquires can be made to Dr Iosif Mporas, Director Research Centre Networks and Security.

Please download and complete an application form

In section 11 you must provide a comprehensive personal statement of up to 500 words describing your motivation to do research on this project at the University of Hertfordshire, and providing information on how you meet any of the essential or desirable requirements described above.

Please also send with your application form:

  • A research proposal not exceeding 1 page.
  • Two academic references.
  • Copies of qualification certificates and transcripts.
  • Certification of English language competence (minimum IELTS 6.5 or equivalent) for candidates for whom English is not their first language.

Your completed application should be emailed to Ms Amy Bird to the Doctoral College.

You should arrange for your referees to write separately using the form provided and send their reference via email directly to the Doctoral College

Closing date for applications: 31 March 2024.

Interview dates: beginning of April 2024.

Expected studentship start date: 1 May 2024 (or as soon as possible thereafter).

Funding information

The award includes cover for UK home or international tuition fees and a stipend at standard UKRI rates. For 2023-2024 this is set at £18,622. The stipends usually increase annually in line with inflation. Applicants from outside the UK or EU are eligible.