An exciting fully-funded PhD opportunity is available at the University of Strathclyde, based within the Centre for Continuous Manufacturing and Advanced Crystallisation (CMAC). The project, supervised by Mohammad Salehian and Prof. Daniel Markland, focuses on developing a data-driven approach to assess amorphous and co-amorphous API-excipient compatibility, a critical challenge in pharmaceutical development. The current empirical methods are slow and resource-intensive; this project aims to accelerate early-stage formulation by integrating machine learning models that predict drug-excipient compatibility directly from molecular structure. This PhD is part of the 2026 cohort for the Centre for Doctoral Training in Cyber-Physical Systems for Medicine Development and Manufacturing (CEDAR CDT). The research will combine expertise in chemical engineering, pharmacy, data science, and artificial intelligence, offering a multidisciplinary environment at the forefront of pharmaceutical innovation. The successful candidate will join a vibrant research community at CMAC, University of Strathclyde, and benefit from advanced training in cyber-physical systems, automation, and machine learning for pharmaceutical applications. Eligibility: This round is open to UK/Home candidates only. Applicants should have a strong background in chemical engineering, pharmacy, computer science, or a related field. Experience with machine learning, data science, or pharmaceutical formulation is desirable. Funding: The position is fully funded for eligible UK/Home candidates, covering tuition and stipend. Further details are available via the application link. Application Window: Applications open on 17 November 2025, with a deadline of 5 December 2025 for the first round. Future rounds may include opportunities for international candidates. For more information and to apply, visit the application link provided.