Postdoc position for data-driven targeted therapy prediction in the bioinformatics group at BIO@SNS, Pisa

We are looking for an enthusiast, early-career post-doctoral researcher to join the Bioinformatics group of the Laboratory of Biology (Bio@SNS), Scuola Normale Superiore, Pisa (ITALY). Our main research focus is the understanding of the function of protein systems involved in signal transduction using structural, network and evolutionary bioinformatics as well as machine learning analysis of omics datasets, to develop targeted therapies in both neurodegenerative disorders and cancer. The group is based at the “Area della Ricerca CNR” research campus, embedded in the vibrant research community of Pisa, which includes some of the top Italian universities and research Institutions.

Project Description

The successful candidate should have a strong background in biology and informatics, with a keen interest in biological mechanisms and data analysis, which he/she will exploit to find new therapies for brain disorders. He/she will be involved in a research program aimed at interrogating high-dimensional datasets (e.g. bulk and single cell RNAseq) using state-of-art bioinformatics and machine learning techniques as well as implementing customized pipelines through the usage of cutting-edge deep-learning frameworks, such as graph neural networks. The overarching goal of the project is to find precision therapies by modeling responses for specific genomics profiling. The project will be carried out in collaboration with experimental and clinical research groups in Italy and abroad.

Key Tasks And Responsibilities
  • Customizing available R or Python workflows for the analysis of bulk and single cell RNAseq;
  • Development and application of customized statistical and machine learning pipelines for data integration and for prediction of therapy responses based on omics profiles;
  • Interpretation of these datasets and generated outputs through computational functional genomics techniques (e.g. protein interaction network and pathways analysis) to infer mechanisms of action;
  • Collaborating with expert biologists and groups members to define amounts and type of data used in each project;
  • PhD in a relevant subject area (Computational Biology, Bioinformatics, Biology, Chemistry, Computer Science, Statistics, Mathematics);
  • full working proficiency in a scripting language (e.g. Python, R);
  • full working proficiency in UNIX/Linux;
  • knowledge of molecular biology and genomics;
  • knowledge of data analysis and machine learning libraries (e.g. pandas, scikit-learn, pytorch, tensorflow, keras);
  • knowledge of Bioconductor packages for (single cell) RNAseq analysis pipelines (e.g.DESEQ2, Seurat);
  • knowledge of databases such as GEO, TCGA, GTEX, GDSC, DepMap, L1000,OpenTarget Genetics;
  • Protein interaction network analysis through scripting (igraph, networkx) or Cytoscape;
  • experience with HPC computing;
  • knowledge of statistics and combinatorics;
  • fluent in English;
  • ability to communicate ideas and results effectively;
  • ability to work independently and to organise own workload;
  • ability to work with others in a collegial manner and to communicate effectively internally at all levels and with selected external individuals;
  • ability to work in a multi-cultural, multi-ethnic environment with sensitivity and respect for diversity.
Following Requirements Will Be Also Favourably Considered
  • structural bioinformatics;
  • chemoinformatics;
  • knowledge of scRNAseq analysis pipelines (e.g. Scanpy, Seurat);
  • full working proficiency in a compiled language (e.g. C, C++, Fortran);
  • ability to deliver effective presentations and scientific talks;
  • ability to devise novel quantitative models;
  • experience in formulating the world in statistical models and applying them to real data;
  • strong publishing record;
How to apply

The position is for two years and is available from spring 2023 or as soon as possible hereafter.

Please apply directly here:


For informal inquiries please write to:


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