Senior Computational Biologist

Senior Computational Biologist

Champalimaud Foundation

Lisbon, Portugal

The Cancer Dormancy & Immunity lab headed by Ana Luísa Correia is seeking a highly motivated computational biologist to join us on a project supported by an AACR Trailblazer grant.

The position focuses on applying cutting edge computational methods to understand neuro-immune and neuro-stromal interactions in the context of liver physiology and disease, particularly during metastasis formation. Spanning single cell transcriptomics, spatial transcriptomics and functional genomics, the fellow will lead large scale integrative analyses to delineate the complex and overlooked role that neuro immune and neuro-stromal interactions might play in cancer progression. While this position is computational, the project is highly collaborative, involving regular interaction with wet laboratory colleagues in Cancer Biology, Immunology, Neuroscience and Translational Medicine.

The Opportunity:

• Work closely with computational and experimental colleagues to apply cutting edge computation to impactful scientific problems in cancer biology;
• Leverage multimodal high dimensional data (including single cell and spatial transcriptomics) to investigate the role of neuro-immune and neuro-stromal interactions in the formation of liver metastases;
• Exploratory research with the potential for real impact on patients;
• Present at scientific conferences and publish scientific insights.

Your qualifications:

• Ph.D. in Computational Biology, Bioinformatics, Computer Science, Cancer Genomics or related field;
• Formal training in mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc.;
• Strong programming skills, preferably in Python and/or R;
• Experience with single-cell and/or spatial transcriptomics data analysis (including cell type annotation, differential abundance/accessibility/expression testing, spatial analysis, and trajectory inference);
• Proven ability to bridge bioinformatics and experimental work to solve complex problems, ideally within liver health or cancer;
• Demonstrated ability to clearly and effectively communicate complex computational findings to both technical and non-technical audiences through compelling visualizations;
• Demonstrated ability to rigorously document all analyses, code and workflows, including the usage of GitHub and Jupyter/R notebooks;
• Independent, highly motivated, and highly collaborative with the ability to work together with multi-disciplinary teams of computational and laboratory biologists.