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Personalized pharmaceutical manufacturing: Industry 4.0 technologies

Centre for Accountable, Responsible and Transparent AI

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Dr Uriel Martinez Hernandez , Dr Bernardo Castro Dominguez , Dr H Leese , Prof Chick Wilson No more applications being accepted Competition Funded PhD Project (Students Worldwide)
Bath United Kingdom Artificial Intelligence Chemical Engineering Data Science Electrical Engineering Electronic Engineering Mechanical Engineering Pharmaceutical Chemistry Robotics

About the Project

Biosensors and machine learning methods can be used to generate physiological and biochemical data from individuals for the identification of illnesses and physical conditions. The identification process from the smart combination of biosensors and machine learning can be employed for manufacturing customised medical treatments. This process requires modular manufacturing technologies such as robotics and 3D printing, which offer rapid small scale production, efficient customization and digital control. This smart manufacturing of personalised medicines offers efficient treatments, security against supply chain disturbances, and novel and enhanced healthcare economic models.

We envision that to develop the concept of personalized medicine, the integration of innovative modular pharmaceutical manufacturing robotics, personalized data and machine learning must be performed in a safe, transparent and secure manner. Consequently, this multidisciplinary project offers the opportunity to develop a smart robotic manufacturing technology by exploring the integration of machine learning algorithms for process control, drug customization, personal data handling and security.

This project is aligned with the UKRI Centre for Doctoral Training (CDT) in Accountable, Responsible and Transparent Artificial Intelligence (ART-AI) and involves the following key research tasks:

· Research and development of machine learning methods using data from biosensors attached to the human body to identify the optimal medical treatment for a patient. This approach will include modules to ensure the safe and responsible processing, management, and identification of medical treatment for the patient.

· Research and development of a smart robotic platform capable of 3D printing medicines with the specifications and needs of the patient identified by the machine learning methods. The manufacturing process will be transparent at all times, allowing the user to trust in this smart manufacturing technology. The computational methods and robotic platform developed in this project will be implemented and tested at the Centre for Autonomous Robotics (CENTAUR), the Department of Chemical Engineering and the Department of Computer Science.

The research to be undertaken in this project has a strong multidisciplinary nature. Therefore, the student is expected to collaborate with students and researchers from ART-AI, CENTAUR, the Centre for Circular and Sustainable Chemical Technologies (CSCT), the Continuous Manufacturing Hub (CMAC), Computer Science, Electronic and Electrical Engineering, Chemical Engineering and Mechanical Engineering. Furthermore, the student is expected to attend multiple events such as conferences, workshops and publish the results from the research work in international conferences and journals.

Candidates should have, or expect to complete, an MSc or MEng in Robotics, Computer Science, Electronics, Mechanics, Mathematics, Physics or related areas.

Further details of the ART-AI CDT can be found at:

Informal enquiries about the project should be directed to Dr Uriel Martinez Hernandez: [Email Address Removed].

Enquiries about the application process (detailed at should be sent to [Email Address Removed].

Formal applications should be made via the University of Bath’s online application form:

Start date: 4 October 2021.

Funding Notes

ART-AI CDT studentships are available on a competition basis for up to 4 years. Funding will cover tuition fees and maintenance at the UKRI doctoral stipend rate (£15,285 per annum in 2020/21, increased annually in line with the GDP deflator). We offer at least ten studentships each year, up to three of which can be awarded to international students.
We also welcome applications from candidates who can source their own funding.
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