We wish to hire a talented and highly motivated computational biology postdoc to investigate mechanisms underlying heterogeneity of response to personalised cancer immunotherapy.
Cancer immunotherapy by which the patients’ immune system is modulated to find and kill cancer cells, is an area of cancer research that is gaining tremendous momentum. However, not all patients treated with these high-cost approaches benefit and immune-related adverse events can be devastating. Patient care could therefore be substantially improved by better understanding of how and why response to immunotherapeutic approaches vary in different patients. The focus of this project is to develop machine-learning and computational approaches to help us understand mechanisms underlying the heterogeneity of response in cancer patients.
Multiple factors have been reported affecting an immune response to the treatment including mutation burden rate, cytotoxic T cell infiltration, antigen processing and presentation defects, mutation-driven clonal signature and composition of intestinal microbiota. Owing to advances in high throughput sequencing technologies, in particular recent single cell advancements, these features can now be measured from patients’ samples at bulk and single cell level at multiple time points including before, during and after treatment. The post will be specifically focused on developing machine-learning and computational models to study two key contributing factors to the underlying heterogeneity: defects in antigen processing and loading and defects in TCRs recognition of antigens.
The successful applicant will work jointly with the Koohy’s group, specialised in machine-learning and computational biology and the Cerundolo’s group specialised in cancer immunology. The successful candidate will conduct research in a dynamic and highly collaborative environment of an internationally leading research institute. The postholder will benefit from the in-house and within the university expertise in tumour-immunology, computational biology, machine-learning and big-data science.
You will hold a PhD in a relevant subject and have strong background in at least one of bioinformatics, machine-learning, computer science and/or statistics. You will have excellent programming skills in at least one programming language, preferably python and/or R and able to develop novel computational methods, algorithms and data processing pipelines.
The post is full-time and fixed-term until 31 March 2022.
Applications for this vacancy are to be made online. You will be required to upload a supporting statement and CV as part of your online application.
Only applications received before 12.00 midday on 13 May 2019 will be considered.
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