Placement Student – Digital Pharma

CPI helps make great ideas and inventions a reality. We’re a team of intelligent people using advances in science and technology to solve the biggest global challenges in healthcare and sustainability.

Through our incredible people and innovation infrastructure, we collaborate with our partners in industry, academia, government, and the investment community to accelerate the development and commercialisation of innovative products.

From health technologies and pharmaceuticals to sustainable food and materials innovations, we turn the entrepreneurial spirit and radical thinking of our people and partners into incredible impact that makes our world a better place.

CPI has an exciting opportunity for a placement student to join the established and growing digital pharma team. The successful candidate will use process modelling and simulation methods (e.g., gPROMS, ASPEN, STAR-CCM+) alongside advanced data analytics (hybrid modelling methods) to develop pharmaceutical manufacturing process models. The role will also (initially) involve optimising existing models of continuous direct compression (CDC) unit operations such as feeding, blending and tableting.

Key tasks in the role will include (but are not limited to the below), please download the job description for full details available on the CPI careers page:

• Using a variety of modelling techniques, develop predictive tools and models to support drug development/​manufacturing platforms. Modelling techniques include (but not limited to): discrete element modelling (DEM) tools such as STAR-CCM+, process models and flowsheets (e.g., gPROMS, ASPEN), statistical techniques, model predictive control (MPC), population-balance models (PBM), first-principles engineering models, data-driven modelling.
• Engaging with the wider CPI R&D team including data science and process modelling scientists. 
• Supporting colleagues in the design, execution, validation and delivery of modelling projects to a high standard (e.g., good manufacture practice), and preparing models for commercialisation. Working alongside colleagues to provide customer support and consultancy for modelling tools.
• Documenting experimental and modelling data and supporting colleagues in the training of project teams to ensure successful application of process models.
• Working collaboratively with process engineers, chemists, analysts, specialists in instrument automation and control, to apply and implement modelling tools into project workflows, assist with troubleshooting complex manufacturing problems and propose hypotheses to solve issues.
• Working collaboratively with data scientists to assist in application and implementation of hybrid modelling components.
• Supported by colleagues, work collaboratively with external partners (e.g., universities, research organisations), pharmaceutical clients, and vendors (e.g., software companies) to contribute towards the execution and delivery of modelling projects
• Presenting scientific results to a diverse audience through data analysis and visualisations.

Just one example of an exciting project the team has recently worked on is:

Grand Challenge 1 

Continuous direct compression (CDC) is a continuous manufacturing process that allows much greater process control than traditional batch-type pharmaceutical manufacturing methods, enabling rapid production of formulations at a range of scales. In addition, CDC offers shorter optimisation times than batch-type processes and therefore more efficient use of starting materials. GC1 further built on this process efficiency by developing a digitally-twinned CDC platform and workflow, allowing scientists to understand and optimise their formulation process in digital space. This will reduce the amount of starting materials needed for optimisation and reduce the overall cost of the technology for the end-user.

The ideal candidate should be working towards a degree (or equivalent) in chemical, biochemical, process control, pharmaceutical engineering, biotechnology, physical sciences or a related discipline with a primary focus on mechanistic modelling and;

• Have a good understanding of fundamental engineering/​physical concepts such as mass, momentum, and energy transport phenomenon; and have a mathematical ability to setup and solve linear, non-linear, and differential equations.
• Have a working knowledge of numerical methods, computational modelling of chemical/​physical processes, programming, and mathematics. 
• Have a working knowledge of computational fluid dynamics (CFD), discrete element modelling (DEM) tools such as STAR-CCM+, flowsheet modelling using simulation software like gPROMS Formulated Products, ASPEN or related process simulators, and open-source machine learning and AI technologies applied to process modelling like Python. Matlab & Simulink will be very desirable. 
• Possess good organisational skills, have the ability to manage own workload effectively and support with several projects simultaneously, delivering results in a timely manner.
• Be able to work effectively as part of cross functional teams and embrace teamwork.
• Have strong written and verbal communication skills: must be able to impart ideas and results with colleagues with diverse backgrounds.

Applications would also be welcomed by candidates who have;

• Technical experience in model predictive control software like PharmaMV.
• Technical expertise in the use of Process Analytical Technology (PAT), including equipment, methods, models and data structure requirements.
• Experience with discrete element method (DEM) and computational fluid dynamics (CFD).
• Experience with any of the following software is preferred: ASPEN, EDEM, LIGGGHTS, XPS, CFDEM, ANSYS Fluent, OpenFOAM, STAR-CCM+, other closely related packages. 
• Knowledge of data analysis and visualisation packages (e.g., Spotfire or Tableau) is desirable. Strong statistical and machine learning skills are also preferred.
• Experience with developing dashboard tools using software systems like PowerBI.
• Experience with Code Management Software like GitHub.
• Knowledge of the software development workflow and commercialisation of modelling components like machine learning regression/​classification models.
• Knowledge of Design of Experiment approaches, AWS tools, database access and query language.

At CPI, we offer a wide range of benefits to our employees, this includes:

• Up to 36 days holiday, including bank holidays – Plus a holiday purchasing scheme
• Generous pension scheme
• Life assurance and accident insurance schemes
• Flexible working
• Learning and Development Opportunities
• Free parking

Find out more about our culture and benefits.

The Company

At CPI we work with our partners to translate inventions into products and processes that enhance health and well-being, protect, and improve our environment and increase productivity across industries.

With a deep understanding of innovation processes and funding, outstanding technical expertise, and industry relevant assets, we enable products and processes to be quickly and cost-effectively brought to market. This supports the development of next-generation manufacturing, highly skilled jobs, and economic growth for the UK.

Our partners operate across many diverse markets including pharmaceuticals, speciality chemicals, food and drink, electronics, and transportation. 

The Benefits

At CPI, we offer a wide range of benefits to our employees, including family friendly policies, flexible holidays and holiday purchasing schemes, opportunities for learning and development, generous pension plan, life assurance and accident insurance schemes. 

How to Apply

The closing date is 23.59pm on Sunday 20 October 2024. 

If you wish to apply for this position, please submit your Cover Letter and CV through our careers page at www​.uk​-cpi​.com/​c​a​reers.

CPI is an organisation based in the UK. Commencement of employment is conditional to demonstrating right to work in the UK, sponsorship may be available.