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PhD Studentship in the Institute of Neuroscience: Development and validation of gait digital outcomes as clinical endpoints in Parkinson’s disease


Project Description

Number of awards:

1

Start date and duration:

April 2019 for three years.

Overview:

Interested in working as part of a large international consortium of Academic and industry partners? Do you like working with wearable technology and understanding how the way we walk can help us predict how (well or badly) we age?

This PhD provides the unique opportunity to work within the Innovative Medicines Initiative (IMI) MOBILISE-D consortium.

The overarching objective of MOBILISE-D is linking digital assessment of mobility to clinical endpoints to support regulatory acceptance and clinical practice. MOBILISE-D’s results will facilitate optimal clinical mobility management through personalised healthcare.

MOBILISE-D consists of 35 Academic and EPFIA partners, from 11 countries with long, successful collaboration, combining technical and clinical expertise.

The PhD will focus on developing, implementing and validating digital mobility outcomes (e.g. real walking speed) derived from digital devices data collected in controlled and free-living conditions to predict relevant clinical outcomes.

You will be expected to work in an interdisciplinary group who has an established international reputation in gait analysis and wearable technology analytics: the Brain and Movement (BAM) Research Group at the Institute of Neuroscience.

BAM Research Group are based in the Clinical Ageing Research Unit, a translational clinical research platform jointly run and managed by Newcastle University and Newcastle Hospitals NHS Foundation Trust.

Sponsor:

European Union Horizon 2020 Joint Technology Initiative, IMI-Innovative Medicines Initiative-Call13, topic 7 (EU-H2020-JTI-IMI2)

Name of supervisor(s):

Professor Lynn Rochester, Dr Silvia Del Din (https://www.ncl.ac.uk/ageing/staff/profile/silviadel-din.html#background), Dr Alison Yarnall.

Eligibility Criteria:

This award is available to UK/EU applicants.

Essential Qualifications

•At least a 2:1 honours degree or international equivalent, in a subject pertaining Biomedical Engineering, Movement Science or related field. A further qualification such as an MRes is advantageous.
•Expertise working with wearable sensor data and analysis
•Expertise in understanding and applying developed algorithms for extractions of digital mobility outcomes
•Experience with movement analysis (e.g. locomotion)
•Knowledge of statistical techniques
•To study this course you need to meet the following English Language requirements: IELTS 6.5 overall (with a minimum of 5.5 in all other sub-skills).

Desirable

•Knowledge of gait analysis
•Knowledge of mathematical and machine learning techniques
•Experience in working with older adults
•Expertise in independently developing novel algorithm for extractions of digital mobility outcomes (e.g. gait outcomes).

How to apply:

You must apply through the University’s online postgraduate application system.

To do this please ‘Create a new account’ (https://www.ncl.ac.uk/postgraduate/apply/).

Only mandatory fields need to be completed. However, you will need to include the following information:
•click on programme of study
•insert in the 8300F programme code section and click search
•select Programme name ‘PhD in the Faculty of Medical Sciences (full time) – Neuroscience’
•insert the studentship code IN106 in the studentship/partnership reference field
•attach a covering letter and CV. The covering letter must state the title of the studentship and reference code IN106 explaining how your interests and experience relate to the project
•attach degree transcripts and certificates and, if English is not your first language, a copy of your English language qualifications.

Contact:

For further information, please email Dr S Del Din, Institute of Neuroscience (IoN) - BAM Research Team: https://research.ncl.ac.uk/bam/

Funding Notes

Full award covers 100% of UK/EU tuition fees and annual stipend of £15,000.

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