Mohammad Salehian's research focuses on integrating optimisation algorithms and machine learning techniques in designing pharmaceutical products to improve the way medicines are made. He obtained his BSc (2015) from the Sharif University of Technology, Iran, MSc (2018) from Istanbul Technical University, Türkiye, and PhD (2022) from Heriot-Watt University, UK, with a focus on computational modelling, numerical optimisation and applied machine learning (ML) in petroleum and natural gas engineering. Mohammad secured the Republic of Turkey's government-funded higher education scholarship, which allowed him to start his research career in the application of ML-assisted optimisation methods in oil and gas production. During his PhD, he was sponsored in the "Value of Advanced Wells (VAWE)" joint industry project by Woodside Energy and CNOOC International, where he continued his research in the interface of numerical optimisation, machine learning, and petroleum engineering. In 2022 he joined Daniel Markl's group as a Research Associate at the University of Strathclyde after completing his PhD. He works at the Made Smarter Innovation | Digital Medicines Manufacturing Research Centre (DM²), as a part of CMAC Future Manufacturing Research Hub at the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS). 

• Computational scientist with an engineering background specializing in modeling and simulation, machine learning, and optimization. Experienced in developing digital solutions for pharmaceutical products manufacturing, chemical processes, and subsurface energy systems (oil and gas production).
• Initiated/co-led several collaborative research projects and disseminated the results with best-in-class journal/conference papers and presentations.
• A quadrilingual (fluent speaker of four languages), thanks to the history of research, teaching in higher education, and academic supervision at the top universities of the UK, Türkiye, and the Middle East.
• Author of 20+ publications and international conference presentations/talks. Achieved various awards including scholarships, studentships, and bursaries at all universities attended, UK Global Talent Visa, UK top 10 doctoral theses of the year 2021 by Association of British Turkish Academics, four best paper/poster awards, and two travel grants.

Mohammad's research interests lie in the application of the following computational technologies in pharmaceutical manufacturing, chemical processes, and subsurface energy systems:

• Model-based Numerical Optimisation (Model Predictive Control)
• Machine Learning
• Computational (Analytical/Numerical/Surrogate) Modelling
• Data Analytics