PhD Vacancy: Plant-wide dynamic mathematical modelling and optimisation of integrated continuous pharmaceutical manufacturing processes

Computational tools and model-based optimisation, control and more broadly decision-making methods and applications have grown dramatically over the last decade and opened opportunities for a new generation of digital representation and simulation tools referred to as Digital Twins. A Digital Twin provides a virtual and yet a living and interactive replica of a physical system, process or product. It offers an augmented simulation and visualisation platform and expected to become a standard capability in all industries in near future. The pharmaceutical and biopharmaceutical industries are undergoing a paradigm shift with the development and adoption of more flexible regulatory tools, agile lean and cost-effective continuous manufacturing technologies as well as robust decision-making systems. There are urgent and unprecedent needs for more reliable and predictable simulation tools for model-based design, optimisation and control which came with a real transformation of the pharmaceutical job market.
This PhD project will look at the development and validation of new strategies to build high fidelity dynamic models and integrated digital twins of a continuous pharmaceutical processes with self-optimising capabilities. The focus of the project will be mainly modelling and simulation but also potentially experimental validation that can be conducted by the PhD student or research collaborators. This PhD Project will benefit from our strong and well-established expertise in mathematical modelling, simulation and process control. It will also be conducted as part of the Future Continuous Manufacturing and Advanced Crystallisation Research Hub (CMAC HUB), a world-class consortium involving more than 30 industrial and academic partners, including 8 Big Pharma companies (e.g. GSK, Novartis, Astra Zeneca, Roche, Pfizer). Initial studies would focus on a continuous crystallisation stage, but then the methodology would be extended to include downstream isolation steps, leading to a seamless fusion of physical and data-driven model implementations.

Primary supervisor: Dr Brahim Benyahia  (Department of Chemical Engineering)
Second supervisor: Prof Chris Rielly  (Department of Chemical Engineering)

Funding: 3 year studentship starting October 2020
  • UK/EU students: UK/EU tuition fees and the National Minimum Doctoral Stipend (£15,009 for 2020/21)
  • International students: fees only and stipend needs to be covered by a different source

How to Apply
: Applicants should send a CV, contact details of 2 references and a covering letter to Dr Brahim Benyahia,

Industry Vacancies

AstraZeneca Associate Principle Scientist- Process Modelling

Are you a scientific leader who wants to drive process modelling and simulation in process development and optimisation? Would you like to apply your expertise to impact the Chemical Development in a company that follows the science and changes the life of patients all over the world? Then AstraZeneca might be the one for you!

Closing Date: 26th April
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Associate Principal Scientist - Process Modelling

Welcome to Macclesfield one of over 400 sites here at AstraZeneca, providing a collaborative environment where everyone feels comfortable and able to be themselves is at the core of AstraZeneca’s priorities, it’s important to us that you bring your full self to work every day. To help you maintain your best self, here’s a sneak peek into some of the things this site provides for you: gym access, networking events both in form of after-works and lunch & learns. We also offer you a sustainable office working environment which is bright and spacious.

Your Business Area
In Chemical Development we believe that nothing is impossible, and we are determined to push the boundaries of science to deliver medicines to our patients. We combine people’s technical knowledge and understanding with their talent and drive to design, develop and optimise synthetic routes that deliver the active pharmaceutical ingredients (APIs) of our medicines in a sustainable, commercially viable way. We believe in innovation and continuous improvement, working closely with colleagues in academia and across industry, to ensure that we apply the latest technologies and principles in the processes we develop. Our ambition is to establish a strong data driven, prediction, modelling, and simulation culture within the department to carry out our research and experimental work in the most efficient way and maximise learning from data generated. To achieve our bold ambition, we have an open position for an Associate Principle Scientist in Process Modelling.

What you’ll do
In this role you will be a scientific leader who will drive process modelling and simulation in process development and optimisation. This to establish strong modelling and predictive culture in our growing Chemical Development. You will work across skillset areas (eg. Process engineering, chemistry, computational chemistry and crystallisation). Your role includes to identify and build on opportunities to apply modelling and simulation into our ways of working. You will feed into the development projects and work closely with the project technical teams, to apply digital approaches to the process design and optimisation resulting in robust and sustainable chemical processes to manufacture APIs.

Key Responsibilities:

Deliver high quality to late stage drug substance development projects (Phase 2 onwards)
Investigate novel algorithms and techniques to answer relevant challenges in drug substance process design and optimisation
Develop, validate and implement modelling protocols to be used by general the wider scientific community
Drive and champion digital transformation and innovation within Chemical Development
Drive scientific excellence internally and externally by presenting current and novel approaches at scientific conferences and authoring scientific publications
Identify and build on opportunities for external scientific collaborations, collaboration with software vendors and lead collaboration projects
Work across subject areas and skill groups within and outside Chemical Development
Be a role model and mentor to scientists with less experience
Develop training material and provide training to AstraZeneca user groups

Essential for the role

You have a degree or PhD in a relevant area such as, chemical engineering, chemistry or similar. You need a strong understanding of engineering principles of unit operations in chemical processes (e.g. mixing, distillation, liquid-liquid extraction, isolation and drying), and have experience of in-silico modelling of such operations. To succeed in your role, significant experience in flow sheeting modelling software such as gPROMs, Aspen Plus and/or others is a must. You have programming proficiency and experience with relevant software tools such as MATLAB, Python, R, Fortran, COMSOL, Pearl, C#, C++ and/or others. You are used to working and collaborating with colleagues with different skillsets. You also need experience working alongside R&D scientists (chemists, analysts, statisticians and crystallisation scientists). You are strategic person, both in your thinking and your approach. In this position your strong communication and influencing skills will be used daily.

Preferred Requirements:

Experience in data curation - using tools such as (Git/Bitbucket)
Knowledge of server-based databases such as SQL or Oracle
Understanding and application of lab automation or control software
Experience working in the Pharmaceutical or Fine Chemicals Industry
Experience leading and driving improvement projects such as establishing new modelling and simulation approaches or ways of working
Experience of working with external partners such as academic or industry peers and/or software developers on investigating new modelling approaches and implementing these within the company
Understanding and application of machine learning, advanced data mining and analytics
Ability to use modelling software (e.g. Computational Fluid Dynamics, Discrete Element Method, DynoChem) to support process understanding
Knowledge and application of physical properties prediction methods such as COSMO-RS, NRTL, NRTL-SAC, UNIFAC, PC-SAFT, SAFT-g Mie
Knowledge of organic chemistry principles linking structure, properties and reactivity
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