Location
Job description
Location Brighton, UK Hours Full-time considered up to a maximum of 1.0 FTE (37.5 hours) Requests for flexible working options will be considered (subject to business need). Salary Grade 8 starting at £47,389 to £56,535 per annum, pro rata if part time. Contract Type Fixed Term Contract About the role
The University of Sussex, in partnership with Custom Pharmaceuticals Ltd (CP), offers an opportunity to develop and embed advanced data analytics and predictive modelling within pharmaceutical product development. This post is fixed term for 30 months and based primarily at CP Ltd’s offices in Brighton. CP is a UK-based contract development and manufacturing organisation supporting clients in bringing new medicines to market. The Knowledge Transfer Partnership (KTP) will support CP in establishing new in-house capability in data analytics and quantitative modelling, enabling more systematic and reliable decision-making across drug product development activities. The role sits at the interface between the company and the University and is central to delivering the objectives of the partnership. At present, many development decisions rely on expert judgement and manual processes, despite the availability of large volumes of process and formulation data. The project will focus on developing and applying advanced analytical and predictive modelling approaches to improve how this data is analysed and interpreted. Initial work will focus on early-stage product development case studies, with the aim of reducing trial-and-error activity, improving development success rates, and shortening development timelines. Working closely with academic supervisors at the University of Sussex and multidisciplinary teams across CP, the post holder will follow CP’s New Product Introduction process to review available datasets, assess current analytical capability, and design predictive models to support formulation and process decisions. These models will be applied to optimise development activities and embedded into client-facing workflows. A key part of the role is to consolidate the modelling and optimisation capability into outputs that demonstrate value to clients and support the first commercial launch of this new service. The post holder will lead day-to-day project activity, report to joint University and company governance structures, and contribute to the transfer of knowledge between academic and industrial partners. The role is expected to support CP’s longer-term strategy by embedding sustainable capability and enabling the development of new data-driven services.
About you
The successful candidate will have a strong quantitative background in mathematics, statistics, informatics, or a related discipline, and experience in applying analytical methods to data-intensive problems. They will typically hold a PhD or master degree with industrial experience, with a PhD or equivalent research experience being desirable. They will have experience in mathematical and statistical modelling, including optimisation and decision-making under uncertainty, and be proficient in Python and/or R. They will be confident in handling, analysing, and interpreting large datasets, and able to communicate quantitative results clearly to non-specialist audiences. They will be able to work independently, manage competing priorities, and meet deadlines, while contributing effectively within multidisciplinary teams. They will have well-developed organisational and communication skills, and an interest in research dissemination, knowledge exchange, and collaborative working across academic and industrial settings.
About our School
We are a research-intensive School that simultaneously delivers outstanding teaching to our students. As such we aim to be consistently recognised as truly world leading in key areas of our science, by extending the international excellence, impact and visibility of our research. We aim to be within the top UK universities in our discipline areas, attracting high-quality students at both undergraduate and postgraduate levels. We will establish a reputation as having a particularly open, stimulating, collaborative and supportive working environment for both staff and students, enabling the best possible outcomes for research, teaching and learning. We will further enhance our links with non-academic actors on local, national and international scales, and will maximize the impact of our work.
The School of Mathematical and Physical Sciences is proud to hold a Bronze Athena Swann Award. A Knowledge Transfer Partnership (KTP) is a three-way collaboration between a company, a university, and a graduate (known as an Associate) that enables the transfer of knowledge, technology, and skills the company does not currently have. Supported by the University, KTP Associates manage strategically important projects within the business. Associates are supported by academic supervisors, company staff, and an independent KTP Adviser, who assists with partnership working and professional development. The programme also provides access to management training, mentoring, and a personal development budget. For more information, visit the Innovate UK website.
Why work here
Our university is situated off the A27, next to the beautiful South Downs where you will enjoy everything that our 150-acre campus has to offer. We are accessible by public transport; Falmer train station is a five-minute walk to campus and several bus stops are located within campus. We also have dedicated cycling paths and encourage our staff to use these with our offering of a cycle to work scheme. Sussex is a renowned, multi-accredited, research-led International University and this is only possible because of the people that work here. Whether you are a member of Faculty, part of a Professional Services team or a Student, it’s our people that make us great and we want you to be part of that.
Find out about our equality, diversity and inclusion.
Further Key Information
Please contact Ivor Simpson (I.Simpson@sussex.ac.uk), Marianna Cerasuolo (M.Cerasuolo@sussex.ac.uk), or Archie Kubba (a.kubba@sussex.ac.uk) for informal enquiries. KTP is funded by a UK Government grant, and this appointment carries no expectation of extension beyond the end of the fixed-term contract. The University is committed to equality and valuing diversity, and applications are particularly welcomed from women and black and minority ethnic candidates, who are under-represented in academic posts in Science, Technology, Engineering, Medicine and Mathematics (STEMM) at Sussex. The University of Sussex values the diversity of its staff and students, and we welcome applicants from all backgrounds. Eligibility
Visa Sponsorship Queries: This role may be eligible for sponsorship under the Skilled Worker route. The assigned SOC code is 2119 – 'Natural and social science professionals n.e.c.' and the going rate is £41,500. If the successful candidate requires a sponsored visa for this role, they will need to obtain ATAS clearance prior to the employment start date. This post may also be eligible for the Global Talent visa, depending on the individual circumstances of the successful candidate. In principle, previous KTP Associates are not eligible to apply. There are circumstances in which a previous Associate can apply for a new KTP. This must be checked with their previous KTP Adviser. The University requires that work undertaken for the University is performed in the UK.
May 28, 2026 5:59 PM Expected Interview Date 10 June 2026 Expected Start Date 01 August 2026. Fixed Term Contract Duration Fixed Term for 2 years.
The University of Sussex, in partnership with Custom Pharmaceuticals Ltd (CP), offers an opportunity to develop and embed advanced data analytics and predictive modelling within pharmaceutical product development. This post is fixed term for 30 months and based primarily at CP Ltd’s offices in Brighton. CP is a UK-based contract development and manufacturing organisation supporting clients in bringing new medicines to market. The Knowledge Transfer Partnership (KTP) will support CP in establishing new in-house capability in data analytics and quantitative modelling, enabling more systematic and reliable decision-making across drug product development activities. The role sits at the interface between the company and the University and is central to delivering the objectives of the partnership. At present, many development decisions rely on expert judgement and manual processes, despite the availability of large volumes of process and formulation data. The project will focus on developing and applying advanced analytical and predictive modelling approaches to improve how this data is analysed and interpreted. Initial work will focus on early-stage product development case studies, with the aim of reducing trial-and-error activity, improving development success rates, and shortening development timelines. Working closely with academic supervisors at the University of Sussex and multidisciplinary teams across CP, the post holder will follow CP’s New Product Introduction process to review available datasets, assess current analytical capability, and design predictive models to support formulation and process decisions. These models will be applied to optimise development activities and embedded into client-facing workflows. A key part of the role is to consolidate the modelling and optimisation capability into outputs that demonstrate value to clients and support the first commercial launch of this new service. The post holder will lead day-to-day project activity, report to joint University and company governance structures, and contribute to the transfer of knowledge between academic and industrial partners. The role is expected to support CP’s longer-term strategy by embedding sustainable capability and enabling the development of new data-driven services.
About you
The successful candidate will have a strong quantitative background in mathematics, statistics, informatics, or a related discipline, and experience in applying analytical methods to data-intensive problems. They will typically hold a PhD or master degree with industrial experience, with a PhD or equivalent research experience being desirable. They will have experience in mathematical and statistical modelling, including optimisation and decision-making under uncertainty, and be proficient in Python and/or R. They will be confident in handling, analysing, and interpreting large datasets, and able to communicate quantitative results clearly to non-specialist audiences. They will be able to work independently, manage competing priorities, and meet deadlines, while contributing effectively within multidisciplinary teams. They will have well-developed organisational and communication skills, and an interest in research dissemination, knowledge exchange, and collaborative working across academic and industrial settings.
About our School
We are a research-intensive School that simultaneously delivers outstanding teaching to our students. As such we aim to be consistently recognised as truly world leading in key areas of our science, by extending the international excellence, impact and visibility of our research. We aim to be within the top UK universities in our discipline areas, attracting high-quality students at both undergraduate and postgraduate levels. We will establish a reputation as having a particularly open, stimulating, collaborative and supportive working environment for both staff and students, enabling the best possible outcomes for research, teaching and learning. We will further enhance our links with non-academic actors on local, national and international scales, and will maximize the impact of our work.
The School of Mathematical and Physical Sciences is proud to hold a Bronze Athena Swann Award. A Knowledge Transfer Partnership (KTP) is a three-way collaboration between a company, a university, and a graduate (known as an Associate) that enables the transfer of knowledge, technology, and skills the company does not currently have. Supported by the University, KTP Associates manage strategically important projects within the business. Associates are supported by academic supervisors, company staff, and an independent KTP Adviser, who assists with partnership working and professional development. The programme also provides access to management training, mentoring, and a personal development budget. For more information, visit the Innovate UK website.
Why work here
Our university is situated off the A27, next to the beautiful South Downs where you will enjoy everything that our 150-acre campus has to offer. We are accessible by public transport; Falmer train station is a five-minute walk to campus and several bus stops are located within campus. We also have dedicated cycling paths and encourage our staff to use these with our offering of a cycle to work scheme. Sussex is a renowned, multi-accredited, research-led International University and this is only possible because of the people that work here. Whether you are a member of Faculty, part of a Professional Services team or a Student, it’s our people that make us great and we want you to be part of that.
Find out about our equality, diversity and inclusion.
Further Key Information
Please contact Ivor Simpson (I.Simpson@sussex.ac.uk), Marianna Cerasuolo (M.Cerasuolo@sussex.ac.uk), or Archie Kubba (a.kubba@sussex.ac.uk) for informal enquiries. KTP is funded by a UK Government grant, and this appointment carries no expectation of extension beyond the end of the fixed-term contract. The University is committed to equality and valuing diversity, and applications are particularly welcomed from women and black and minority ethnic candidates, who are under-represented in academic posts in Science, Technology, Engineering, Medicine and Mathematics (STEMM) at Sussex. The University of Sussex values the diversity of its staff and students, and we welcome applicants from all backgrounds. Eligibility
Visa Sponsorship Queries: This role may be eligible for sponsorship under the Skilled Worker route. The assigned SOC code is 2119 – 'Natural and social science professionals n.e.c.' and the going rate is £41,500. If the successful candidate requires a sponsored visa for this role, they will need to obtain ATAS clearance prior to the employment start date. This post may also be eligible for the Global Talent visa, depending on the individual circumstances of the successful candidate. In principle, previous KTP Associates are not eligible to apply. There are circumstances in which a previous Associate can apply for a new KTP. This must be checked with their previous KTP Adviser. The University requires that work undertaken for the University is performed in the UK.
May 28, 2026 5:59 PM Expected Interview Date 10 June 2026 Expected Start Date 01 August 2026. Fixed Term Contract Duration Fixed Term for 2 years.
