Position funded by Fundación 'la Caixa' (HR25-00861)

CNAG – The Institute

The CNAG Consortium carries out large-scale projects in DNA/RNA analysis for the improvement of quality of life in collaboration with the Spanish, European and International Research Community. The Centro Nacional de Análisis Genómico (CNAG) is one of the largest Genome Sequencing Centers in Europe, with several Illumina and Oxford Nanopore Technologies sequencers, state-of-the-art single-cell equipment, and an extensive informatics infrastructure. The operation is certified ISO 9001 and accredited ISO 17025, delivering standardized, high-quality data for research and clinics. The CNAG has over 125 members, structured in different units such Bioinformatics, Sequencing, Research and Corporate Management, and participates in major International Genome Initiatives and EU-funded projects.

The Role

We are hiring a Senior Computational Biologist to lead innovative research at the intersection of spatial and single-cell multiomics, machine learning, and precision oncology. The successful candidate will drive the development of computational approaches to characterize the tumor microenvironment and identify mechanisms of treatment response and resistance in colorectal cancer; he/she will join the Single Cell Genomics Group, a highly collaborative international environment with extensive expertise in single-cell and spatial omics technologies, computational biology, and translational genomics.

A key objective of the position will be the development of computational and machine learning frameworks for the integration of spatial transcriptomics, single-cell omics, multi-elemental imaging, proteomic, and clinical datasets to generate a spatially resolved atlas of colorectal cancer before and after chemotherapy treatment. The successful candidate will lead efforts to identify cellular states, cell-cell interactions, and predictive biomarkers associated with treatment response and resistance, while developing interpretable deep learning and foundation model approaches to uncover mechanisms of tumor microenvironment plasticity and therapeutic failure.

Deep learning and biological foundation models hold immense promise for decoding high-dimensional spatial and single-cell datasets, identifying cellular interactions within the tumor microenvironment, and predicting treatment response and disease progression. However, to realize their full translational impact, critical challenges remain, including adapting these models to rapidly evolving spatial and single-cell technologies and overcoming their black-box nature through tailored explainable AI approaches that provide mechanistic biological insights.

The successful candidate will provide scientific leadership in computational oncology, advancing quantitative and machine learning-based approaches to characterize the tumor microenvironment and understand mechanisms of treatment response and resistance in cancer. This position includes scientific and strategic leadership of a dedicated research line focused on deep learning and biological foundation models applied to oncology, immunology and single-cell systems biology.

The candidate will be expected to lead and grow a small team of researchers specialized in machine learning and computational biology, providing supervision, mentoring, and scientific guidance to junior researchers and trainees. The position carries substantial responsibility for defining the computational strategy of the project, driving innovation in multimodal data integration and explainable AI, and establishing CNAG as a leading contributor to the development of machine learning approaches for spatial and single-cell oncology.

Working closely with experimental and clinical collaborators across the consortium, the candidate will independently lead computational research activities from project design through implementation, validation, publication, and dissemination. The role will involve coordinating the analysis and integration of large-scale spatial multiomics datasets, translating computational discoveries into biologically and clinically meaningful insights, and contributing to the identification of novel biomarkers and therapeutic targets for precision oncology.

Whom would we like to hire?

• You have a PhD in Computer Science, statistics, computational biology, or a related quantitative discipline.
• You have a minimum of 3 years of post-doctorate experience.
• You have a strong record of peer-reviewed publications.
• You have experience applying modern deep learning methods (e.g., Variational Autoencoders, Transformers) to biological data. Familiarity with Explainable AI approaches is a plus.
• You have experience in single-cell and spatial omics analysis and data integration workflows (e.g., Harmony, scvi-tools).
• You are proficient in Python and have hands-on experience with PyTorch.
• You have experience with cloud/high-performance computing environments (e.g., GCP, SLURM).

The Offer

·Contract duration: until the end of the project 30/11/2028

·Estimated annual gross salary: Salary is commensurate with qualifications and consistent with our pay scales.

·Target start date: July 2026

·Funding source: The contract will be funded from ”la Caixa” Foundation under the project code HR25-00861 entitled “Advancing Precision Oncology through the Tumor Microenvironment Analysis”

We provide a highly stimulating environment with state-of-the-art infrastructures, and unique professional career development opportunities.

We offer and promote a diverse and inclusive environment and welcomes applicants regardless of age, disability, gender, nationality, race, religion or sexual orientation.

We are committed to reconcile work and family life for our employees and are offering the opportunity to benefit from annual leave and flexible working hours.

Application Procedure

·A motivation letter addressed to Dr Holger Heyn.

·A complete CV including contact details.

·Contact details of two referees.