Organisation/Company Edge Hill University Department Research Office Research Field Other Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Country United Kingdom Application Deadline 15 Aug 2025 - 23:59 (Europe/London) Type of Contract To be defined Job Status Full-time Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Is the Job related to staff position within a Research Infrastructure? No
Offer Description
Edge Hill University is seeking to host an exceptional early career scholar working in the field of motor competence or closely related field for a Marie Skłodowska-Curie European Postdoctoral Fellowship in enhancing the feasibility, practicality, and usability of a tool to assess children’s motor competence in schools. The fellowship is hosted by Dr Richard Tyler (https://research.edgehill.ac.uk/en/persons/richard-tyler ); informal queries are welcomed (tylerr@edgehill.ac.uk ). Dr Tyler has an established track record over the past 10 years in this field and is based in the Department of Sport and Physical Activity where he and his colleagues undertake world-leading and internationally excellent research. Edge Hill University is a picturesque campus-based university located in Ormskirk, Lancashire, UK, 13 miles north of the vibrant city of Liverpool in the low cost-of-living northwest region.
MSCA European Postdoctoral Fellowships last between 1 and 2 years, offers a competitive salary with no teaching obligations, and are open to researchers of any nationality who have not lived in the UK for more than 12 out of the 36 months prior to September 10th, 2025. Salary in accordance with the Marie Sklodowska-Curie Actions Post-Doctoral Fellowships scheme (https://marie-sklodowska-curie-actions.ec.europa.eu/actions/postdoctoral-fellowships ), which is a fixed living and mobility allowance, plus an additional family allowance for applicants with parental responsibilities.
Enhancing the feasibility, practicality, and usability of a tool to assess children’s motor competence in schools
A key consideration for researchers, teachers, and practitioners in the field of child motor competence assessment is obtaining valid and reliable motor competence results in a time- and labour-efficient manner. The administration of many motor competence assessments have been criticised for their high demand in terms of resources (time, equipment, and personnel). Although the Dragon Challenge v1.0 (DC) (https://journals.lww.com/acsm-msse/fulltext/2018/12000/a_dynamic_assessment_of_children_s_physical.12.aspx ) assessment of motor competence is relatively less resource-intensive than many other motor competence assessments, this still needs to be improved to enhance its feasibility and practicality at scale within schools. This MSCA post-doctoral fellowship opportunity seeks to address this through the two linked work packages (WP) proposed below.
Aims:
* To revise the current motor competence assessment to create a “DC short version” without compromising the rigour and psychometric properties of the original tool (WP1)
* Application of machine learning/deep learning/AI to assess motor competence (using “DC short version”) from video data (WP2)
Brief overview of WP1: Revised shorter motor competence assessment
* Pooling of existing motor competence data from DC
* Running factor analysis to confirm a reduced version of the DC (potential output 1)
* Piloting of new revised “DC short version” (potential output 2)
o Examining association of new revised “DC short version” and outcomes
o Validity and reliability evidence for the “DC short version”
* Creating material for new revised “DC short version” – manual, scoring, grading/banding for awards, and training
* Training material and CPD workshops for teachers to upskill as assessors and administrators (leads into WP2)
Brief overview of WP2: Assessing motor competence via video using vision-based human activity recognition
* Following CPD/teacher workshops on conducting the new revised “DC short version” – teachers will record video recordings of participants taking part in “DC short version” – school years 3 to 9 (both primary & secondary school (Key Stages 2 & 3)).
* Continuous direct observation of video files (using vision-based human activity recognition) alongside automated state-of-the-art machine learning (ML) and/or deep learning (DL) algorithms to code DC criteria/scores and align with trained assessor scores (potential output 3)
* ML/DL models will be optimised to classify DC scores/awards and applied to video data. ML/DL models will be examined (potential output 4)
* Creation of application/tool/system for automated DC assessment
Where to apply
E-mail tylerr@edgehill.ac.uk
Requirements
Research Field Other Education Level PhD or equivalent
Skills/Qualifications
Experience of conducting motor competence assessments in various settings and handling/analysing data through statistical methods would be welcomed.
Languages ENGLISH Level Excellent
Research Field Other Years of Research Experience 1 - 4
Additional Information
Eligibility criteria
The applicant/eligibility criteria:
* must have a PhD degree (e.g. PhD in hand by September 10th, 2025)
* must have a maximum of eight years' experience in research, from the date of the award of their PhD degree
* must not have resided or carried out their main activity (work, studies, etc.) in the UK for more than 12 months in the 36 months prior to September 10th, 2025
Selection process
Interested applicants should submit a one-page CV, an outline of the proposed research (maximum 1,000 words), and a cover letter highlighting why you believe you are suited to this project and our Department (maximum 500 words).
Please submit these in Word format by Friday 15th August 2025 to Dr Tyler via email (tylerr@edgehill.ac.uk ).
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