Developing an AI-enabled point-of-care monitoring system to detect systemic concentration of therapeutics in blood

This project is funded by:

  • US Ireland


According to the World Health Organization, around 50 million people worldwide have epilepsy, making it one of the most common neurological diseases globally. Furthermore, they estimate that up to 70% of patient with epilepsy (PWE) could live seizure-free if properly diagnosed and treated. There is a clear unmet clinical need for a point of care (POC) device capable of monitoring antiepileptic drugs (AEDs) levels in patients.

This project aims at developing AI and (bio)signal processing models that can extract relevant inferences from combined high dimensionality surface-enhanced Raman scattering (SERS) and electrochemical data sets. State-of-the-art algorithms based on capsule network, echo state network and graph neural network and/or reinforcement learning enhanced multi-objective co-evolutionary optimisation problems will be explored to address some of the major challenges associated with processing electrochemical and SERS data. This study is part of a larger multi-country, center-to-center research program that aims to develop an AI-enabled, low-cost, multianalyte, POC platform device for detecting the systemic concentration of therapeutics in blood.

The proposed system will improve patient outcomes by measuring systemic AED concentrations to

(1) allow the minimum effective dose that produces long-term seizure control to be identified,

(2) identify under and overdosing that can lead to side effects, and

(3) allow the correlation between the therapeutic concentration and the clinical effects to be understood in an individual patient.

The successful PhD candidate will collaborate with researchers from Dublin City University (IR), Trinity College Dublin (IR) and Texas A&M University (US). The candidate will also benefit from the wide expertise of the university’s Computational Neuroscience, Neurotechnology and Machine Learning community and will gain valuable knowledge in machine learning, transfer learning, high-performance computing, mathematics/statistics and neuroscience and develop skills in commercialization of research outputs for licensing and patents.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

  • Experience using research methods or other approaches relevant to the subject domain
  • A comprehensive and articulate personal statement

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 70%
  • For VCRS Awards, Masters at 75%
  • Experience using research methods or other approaches relevant to the subject domain
  • Work experience relevant to the proposed project
  • Publications - peer-reviewed
  • Experience of presentation of research findings

Funding and eligibility

This project is funded by:

  • US Ireland
  • The scholarship will cover tuition fees at the Home and EU (Ireland) rate and a maintenance allowance of £18,000 tbc per annum for three years (subject to satisfactory academic performance). Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

    Recommended reading

    1.Krasowski, M. D. Therapeutic Drug Monitoring of the Newer Anti-Epilepsy Medications. Pharmaceuticals. 2010. 2.Jacob, S.; Nair, A. B. An Updated Overview on Therapeutic Drug Monitoring of Recent Antiepileptic Drugs. Drugs R. D. 2016, 16 (4), 303–316. 3.Murdoch, W. J.; Singh, C.; Kumbier, K.; Abbasi-Asl, R.; Yu, B. Definitions, Methods, and Applications in Interpretable Machine Learning. Proc. Natl. Acad. Sci. U. S. A. 2019. 4.Chander, A.; Srinivasan, R. Machine Learning and Knowledge Extraction; 2018. 5.Miguel Antonio, L.; Coello Coello, C. A. Coevolutionary Multiobjective Evolutionary Algorithms: Survey of the State-of-the-Art. IEEE Trans. Evol. Comput. 2018. 6.Cooney, C.; Folli, R.; Coyle, D. Optimizing Layers Improves CNN Generalization and Transfer Learning for Imagined Speech Decoding from EEG. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; 2019. 7.Zhou, A.; Qu, B. Y.; Li, H.; Zhao, S. Z.; Suganthan, P. N.; Zhangd, Q. Multiobjective Evolutionary Algorithms: A Survey of the State of the Art. Swarm and Evolutionary Computation. 2011.

    The Doctoral College at Ulster University

    Key dates

    Submission deadline
    Monday 27 February 2023

    Interview Date
    18 April 2023

    Preferred student start date
    18 September 2023


    Apply Online  

    Contact supervisor

    Dr Saugat Bhattacharyya

    Other supervisors

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