Project Title: Constructing mathematical models that predict therapy responses in colorectal cancer
Supervisor: Dr. Dirk Fey
Stipend and fees: Funding is available for four years. The successful candidate will receive a tax-free stipend of €18,500 per annum.
Closing date: 21th December 2018.
Application: Please send a CV, brief cover letter, and contact details of two referees to Dr Dirk Fey (firstname.lastname@example.org). Informal inquiries to the same address are more than welcome.
Brief Description: The project involves the patient-specific modelling of cancer signalling dynamics for improved patient stratification in colorectal cancer, and is part of the EU research consortium COLOSSUS also involving biotech companies and several clinical partners. Envisioned start date: January 2019.
The main goal in this project is an highly integrated computational model for prediction of clinical outcome in colorectal cancer. To achieve this we need to understand the time-dependent input/output relationships of the underlying signalling network, and the processes that shape them. To that end, we will employ both mathematical modelling and experimentation. In particular, we will construct mathematical models of these signalling networks in terms of ordinary differential equations, apply a from us developed patientspecific modelling technology. The initial model construction will be guided by multi-omics highthroughput data from tumour samples available through the COLOSSUS consortium; mathematical modelling and analysis is the main responsibility of the PhD student; experimental validation using cell cultures will be performed internally in SBI; and clinical validation though COLOSSUS partners.
Candidate requirements: • Master degree in systems biology, engineering, mathematics, computer science, physics, or equivalent. • Experience with mathematical modelling using ordinary differential equations. • Knowledge of biological systems, in particular cancer and cell signalling, is preferable. • Motivation, passion, and ability to work in an interdisciplinary research environment.