PhD in Biomedical/Electronic/Neural Engineering

Applications are invited for a highly motivated PhD student to join an exciting new project that will use high density electromyography (EMG) and electroencephalography (EEG) to examine changes in neuroelectric signalling in Amyotrophic Lateral Sclerosis (ALS). This is a 4-year studentship funded by the Trinity Research Doctorate Award.

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a nervous system disease that causes a progressive loss of muscle control. One of the major challenges in the diagnosis and assessment of ALS is the lack of biomarkers to quantify changes in motor function. Recently, research from Academic Unit of Neurology (AUoN) in Trinity College Dublin has shown that information from signals recorded from the brain (EEG) and muscle (EMG) can identify distinct differences in sensorimotor network function in motor neuron diseases (doi: 10.1093/brain/awab322; doi: 10.1093/cercor/bhad152). This project will examine whether features of high density surface EMG signals can be used to detect early signs of motor unit dysfunction.

In this role the successful candidate will use dimension reduction techniques and nonlinear dynamic measures to examine the high density EMG structure. The aim of the project is to assess whether features of the high density EMG signals can sensitively detect subtle signs of motor unit degeneration in people with ALS. To address this aim, the high density EMG analysis will first be refined and tested using simulated data and data from healthy participants, before being applied to signals recorded in people with ALS.

The candidate will join a dynamic and growing team of researchers at the EXG Research Group under the supervision of Dr Lara McManus, hosted in the Trinity Biomedical Sciences Institute 's AUoN. AUoN is a multidisciplinary research group consisting of experts in clinical management, cognition, advanced neuroimaging, epidemiology, and genetics of ALS. The student will work alongside this growing group of ALS researchers and will be able to draw on the clinical and engineering expertise within AUoN.

This position has a required start date of the 1st September 2024, and interviews are expected to take place in April/May 2024. For full details of the post and how to apply see:

https://my.corehr.com/pls/trrecruit/erq_jobspec_details_form.jobspec?p_id=037200

Application Procedure

Applicants should submit:

- A cover letter, to include specific details and evidence of relevant experience/skills and the motivation for pursuing this research area (~1 page)

- Transcripts for all undergraduate and postgraduate courses to date and copies of degree certificates (or equivalent) to show the degree has been or will be awarded

- A full Curriculum Vitae

- Evidence of English language proficiency if required (see www.tcd.ie/study/apply/admission-requirements/postgraduate/)

Application emails should be addressed to:

Name Ms. Kristal MacNamara

Email Address:  [email protected]

Responsibilities

Research and select the appropriate dimension reduction techniques and nonlinear dynamic measures (e.g., mutual information, entropy) to extract information from the HD-EMG signals.

Refine and test parameter selection using simulated EMG data and HD-EMG signals recorded from heathy participants.

Use existing codes to process and analyse the EEG and high density EMG signals (including EEGLab, FieldTrip), and further develop and integrate new algorithms for extracting information from HD-EMG.

Research and evaluate linear and nonlinear methods of estimating corticomuscular coherence between the EEG and HD-EMG signals.

Provide some assistance to team members recording EEG and high density EMG in healthy participants and in people with ALS at our recording lab, located at St. James’s Hospital, Dublin (we will provide the candidate with training in data collection).

Appropriately manage and organise the large neurophysiological datasets and keep accurate, up-to-date, and detailed records for the project.

Meet with supervisor regularly, attend and contribute to research group meetings, journal clubs, and communicate research findings at national and international conferences.

Liaise and collaborate closely with other team members (other PhD students and Research Assistants) of our multidisciplinary research team. 

Prepare research articles for publication in high impact scientific journals. 

Qualifications

The ideal candidate should have, or expect to shortly obtain, a first or upper second class honours (2:1) degree in Engineering (e.g., Neural, Biomedical, or Electronic/Electrical Engineering), Mathematics, Physics, Computational Neuroscience or related quantitative discipline and will be highly motivated and enthusiastic about research. 

Knowledge & Experience (Essential & Desirable)

Essential:

A strong interest in biomedical/neural engineering and neuroscience.

Excellent analytical skills and good coding ability (i.e., Julia, MATLAB, Python).

Desirable:

Experience in signal processing, particularly the analysis of electromyographic (EMG) and electroencephalographic signals (EEG).

Experience working with datasets and conducting data analytics, statistics, using machine learning techniques and/or nonlinear dynamic measures (e.g. entropy, recurrence quantification analysis, etc.).

A solid understanding of the principles of neuromuscular physiology.

Skills & Competencies

Excellent interpersonal and communication skills are essential.

Ability to work well both independently and in a team environment.

Excellent organisational and administrative skills, including a proven ability to work to deadlines.