Mathematical Modeller

University of Oxford
March 11, 2024
Contact:N/A
Offerd Salary:£45,585 - £54,395
Location:N/A
Working address:N/A
Contract Type:fixed term contract
Working Time:Full time
Working type:N/A
Ref info:N/A
Mathematical Modeller

Centre for Human Genetics, Building for Genomic Medicine, Roosevelt Drive, Oxford, OX3 7BN

A highly motivated, ambitious Mathematical Modeller is required to develop and analyse mathematical models that focus on the role of sortilin-mediated progranulin-related pathways in neurodegenerative diseases, reporting to the Oxford-GSK IMCM co-director Professor John Todd and Dr Anna Sher, Director in Quantitative Systems Pharmacology at GSK, and with oversight from the Oxford- GSK IMCM Programme Manager.

This position is a unique opportunity to work in a dynamic environment at the Oxford-GSK IMCM and in close collaboration with Clinical Pharmacology and Modelling and Simulation department at GSK, one of the leading global biopharmas, who are at the forefront of the application of model-informed drug development to clinical trials. You will work closely with members of the Oxford-GSK IMCM Joint Data Team (including machine learning researchers, statistical geneticists and data managers), enabling effective use of the joint data platform.

You will be responsible for the hands-on development, analysis and application of mechanistic mathematical models that will be a step towards enhanced quantitative understanding of the role of anti-sortilin pathways in increasing the progranulin (PGRN) levels and in regulating downstream pathways affecting neurodegenerative disease progression, based on available literature, in vitro imaging and clinical data. You will be tasked with performing simulation studies to understand both the neurodegenerative disease processes and anti-sortilin drug pharmacodynamics, and to contribute to generation and testing of mechanistic hypotheses, and utilisation of Virtual Patients to inform optimal clinical study design including selection of key biomarkers and optimal patient subpopulations. You will ultimately be responsible for supporting the Institute's scientific and strategic objectives and contribute to developing the strategic direction of the Oxford-GSK IMCM.

It is essential that you hold a relevant PhD/DPhil in Applied Mathematics, Mathematical Biology, Engineering, Physics, Computational Biology, Pharmaceutical Sciences, or other related discipline. You will have training or previous experience in building differential equation-based models of physiological pathways or biological systems, quantitative systems pharmacology (QSP), quantitative systems toxicology (QST), semi- mechanistic pharmacodynamic (PD) or physiologically-based pharmacodynamic (PBPK) models. You will also have an understanding of principles and statistical aspects of mathematical modeling and simulation, including sensitivity analysis and parameter estimation techniques. Finally you will have interest in learning new areas of biology, building novel quantitative and computational skills and sharing learnings as well as excellent written and verbal communication skills, including the ability to communicate technical concepts to non-technical audiences.

Applications for this vacancy are to be made online and you will be required to upload a supporting statement and CV as part of your online application. Your supporting statement must explain how you meet each of the selection criteria for the post using examples of your skills and experience.

This position is offered full time on a fixed term contract until 30 September 2027 and is funded by the GSK.

Only applications received before 12 midday on 11 March 2024 will be considered. Please quote 171122 on all correspondence.

Contact Person : Phoebe Astbury Vacancy ID : 171122 Contact Phone : 01865 287775 Closing Date & Time : 11-Mar-2024 12:00 Pay Scale : STANDARD GRADE 8 Contact Email : [email protected] Salary (£) : Grade 8: £45,585 - £54,395 with a discretionary range to £59,421 per annum

From this employer

Recent blogs

Recent news