Research Assistant (Part Time, Fixed Term) (MED

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This review provides an in-depth look at the Research Assistant position within the School of Medicine, offering insights for potential candidates to assess its alignment with their career aspirations.

Opportunity Highlights:

• Pioneering Research: This role offers a unique opportunity to contribute to a cutting-edge, BMA Foundation and Doris Hillier Grant-funded study. The research focuses on a highly relevant and timely topic: investigating the mechanisms by which weight loss, particularly with GLP-1 receptor agonists, improves knee pain in individuals with obesity. This has direct implications for informing future non-surgical treatments for knee osteoarthritis, a condition affecting a significant portion of the population.
• Multidisciplinary Collaboration: You will be immersed in a vibrant, multidisciplinary research environment. This includes working alongside experienced musculoskeletal clinicians and scientists within the Academic Unit of Injury, Recovery and Inflammation Sciences (IRIS), as well as physicists at the Sir Peter Mansfield Imaging Centre. Such collaboration fosters a rich learning experience and exposure to diverse perspectives.
• Advanced Methodologies: The study employs sophisticated research techniques, including advanced imaging (MRI, MR Spectroscopy, musculoskeletal ultrasound), metabolic assessments, and proteomic analysis. This provides an excellent platform for candidates to gain hands-on experience with high-impact methodologies and to deepen their understanding of complex biological processes.
• Translational Research Focus: For those interested in bridging the gap between laboratory findings and clinical application, this role is particularly appealing. It offers a direct pathway to develop experience in translational musculoskeletal research, where findings have the potential for real-world clinical impact.

Key Considerations for Candidates:

• Role Responsibilities: The Research Assistant will be instrumental in various aspects of the study. This includes longitudinal data collection through online surveys, participant recruitment, managing study visits (including imaging acquisition and biological sample collection), ensuring data accuracy and integrity, and contributing to advanced imaging analysis and data interpretation.
• Skill Development: This position is ideal for individuals seeking to enhance their skills in quantitative research methodologies, human participant interaction, and data management. The opportunity to contribute to manuscript preparation and presentation of findings will also bolster scientific communication abilities.
• Academic and Professional Growth: The fixed-term nature of this 18-month post (with a part-time 0.5FTE basis, though higher FTE may be considered) offers a focused period for intensive learning and contribution. This could serve as a valuable stepping stone for further academic pursuits or a career in clinical research.
• Ideal Candidate Profile: Successful applicants will be highly organised with meticulous attention to detail. A genuine interest in musculoskeletal health, obesity, imaging, and metabolic science is crucial. The ability to work effectively with human research participants and a keenness to contribute to impactful research are essential qualities.
• Qualifications: A BSc or equivalent experience in a relevant health-related discipline (e.g., physiotherapy, sport and exercise science, clinical sciences, anatomy) is required. Experience with quantitative research methodologies involving human participants is also necessary. An MSc in a related subject or progress towards a PhD would be advantageous.

This Research Assistant role presents a compelling opportunity for motivated individuals to contribute to significant scientific advancements in musculoskeletal health. The combination of cutting-edge research, a collaborative environment, and the development of advanced research skills makes it an attractive prospect for those looking to advance their career in a meaningful and impactful field.