Fully-funded PhD - Investigating the chronobiological drivers and molecular regulators at the neuromuscular junction during sarcopenia

We invite applications for a fully funded, multidisciplinary doctoral project focused on understanding the mechanistic drivers of sarcopenia, the debilitating age-related loss of muscle mass and function. This research addresses a critical unmet health challenge by exploring a fundamental, underexplored mechanism: how the disruption of the body’s internal circadian clock drives progressive decline at the neuromuscular junction, the vital connection between nerve and muscle.

You will have a unique opportunity to work with a multi-disciplinary team of cell and molecular biologists, chemists and computational experts pioneering research using an advanced, optogenetically controlled human-based nerve-muscle model. Using functional, molecular and metabolomics technologies, integrated with machine learning (ML) and artificial intelligence (AI) workflows, the project aims to create a comprehensive molecular atlas and identify novel, translational biomarkers and therapeutic targets.

The successful candidate will work with the multidisciplinary academic team in the newly built Dalton Building, equipped with state-of-the-art facilities. You will join a vibrant and rich research environment, offering unparalleled training opportunities in advanced cell biology, omics platforms and computational technologies. Your academic journey and development will be supported by our Doctoral College, which creates a supportive and stimulating environment in which our students can thrive.

Project aims and objectives

The core aim of this project is to determine how age-related circadian dysregulation impacts neuromuscular function and muscle homeostasis. This will be achieved by the following objectives:

1. Establishing a co-culture system with rhythmic stimulation to model an aged phenotype, followed by mechanistic interrogation using comprehensive metabolomics, structural, and functional analyses.
2. Using machine learning and artificial intelligence systems to integrate complex datasets, identifying and validating core molecular targets.