Real-time, Multi-attribute of pharmaceuticals using Hyperspectral Imaging for Point of Care manufacturing

A PhD position at the forefront of Pharmaceutics 4.0, combining established manufacturing technologies with cutting-edge machine-learning powered analytics to achieve single-scan, real-time, multi-attribute materials understanding and 100% in-line quality assurance through near-infrared hyperspectral imaging (HSI).

Scientifically, this PhD project will push the frontiers of analytical chemistry and machine learning. You will develop physics-informed, multi-task deep learning models that embed established causal relationships. You will link pharmaceutical composition and microstructure to functional performance while also delivering prediction-time measures of confidence and reliability.

Key Research Areas:

• Develop and optimise a diverse library of solid pharmaceutical products with systematically varied properties.
• Jointly infer chemical, physical, and functional attributes from rich spectral–spatial datasets to enable real-time pharmaceutical analysis.
• Integrate machine learning approaches for data-driven process control and predictive formulation design.
• Combine in vitro studies with in silico modelling to predict key properties of both intermediate (pre-product) materials and final products.

Person specification

A 1st or 2:1 degree (or equivalent) in Pharmaceutics, Biomedical Engineering, Chemical Engineering, Materials Science, or a related field

Strong interest in pharmaceutical manufacturing, digital health, and artificial intelligence

Experience with 3D printing technologies and/or machine learning tools (preferred but not essential)

Excellent communication, analytical, and problem-solving skills

Research training

Dr Alhnan has a leading expertise in pharmaceutical additive manufacturing https://kclpure.kcl.ac.uk/portal/alhnan.html https://scholar.google.com/citations?user=boaKXLsAAAAJ&hl=en

Informal enquiries should be directed to Dr Mohamed A Alhnan:

Alhnan@kcl.ac.uk