Computational Biologist - Microbiome and Cancer
Fully-funded postdoc position for one computational analyst immediately available in the laboratory of Dr. Luigi Nezi at European Institute of Oncology in Milan (Italy). Our Lab aims to developing innovative approaches in cancer prevention and therapy by manipulating the microbiome. We have reported the causality between diversity and composition of the gut microbiome at baseline and therapeutic outcome in patients of melanoma who were treated with immunotherapy (Science 2018). Moreover, our recent studies showed that compositional peculiarities of the tumor microenvironement drive infiltrating T cells into a metabolic dysfunctional state (J Ex Med 2020).
PROJECT: we will integrate metagenomic, metabolomic and functional data from multiple sources to decode the interplay between the microbiome and the host’s immunesystem during tumor progression and response to therapy. Overall, the results of this study will better inform the design of new strategies for cancer prevention and treatment.
CANDIDATE: The candidate will actively interact with worldwide-recognized experts in metagenomic (Dr. Segata Lab) and genomic evolution (Dr. Shaefer Lab) to learn and develop new pipelines.
As part of the project the selected candidate will:
- develop computational approaches to analyze and integrate metagenomic , transcriptomic and metabolomics data from humans;
- build a model to identify biotic and abiotic bacterial components to elicit a systemic antitumor response;
- interact with the team for validation in vitro and in pre-clinical mouse models.
Skills covered in this project:
- 16S sequencing, Metagenomic whole-(shotgun)-genome sequencing, Genomic and transcriptomic analysis, Metabolomic analysis, Multiparametric Analysis of immunological data
The candidate should hold a PhD in Bioinformatics or a similar discipline with a computational or mathematical focus (computer science, physics, mathematics). Good programming skills are required. The researcher should be interested in addressing general questions in molecular and cancer biology and have experience in working with cancer omics data.
Expertise in analyzing metagenomic and/or metabolomic data is a plus. Experience in developing efficient algorithms in the disciplines of optimization, network science or machine learning is a plus.
Competitive salary based on professional experience. Please submit your completed application, including your CV, a short statement of research background/interests, and two references.