Postdoctoral Researcher At The Spatial Biotechnology Research Group

Institute for bioengineering of Catalonia, IBEC

Organisation/Company Institute for bioengineering of Catalonia, IBEC Research Field Biological sciences Researcher Profile Established Researcher (R3) Positions Postdoc Positions Application Deadline 16 Oct 2026 - 23:59 (Europe/Madrid) Country Spain Type of Contract Temporary Job Status Full-time Hours Per Week 37.5 Is the job funded through the EU Research Framework Programme? Other EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description Introduction to the vacant position:

The Spatial Biotechnology Group is looking for a talented and highly motivated Postdoctoral Researcher to work at the interface between experimental and computational biology, building automated, closed-loop workflows. The position is part of our effort to understand how cells spatially organize to drive solid tumor development, and it brings together three fast-moving fronts: spatial proteomics, lineage tracing technologies, and laboratory automation. The successful candidate will design and execute robot-driven experiments and will analyze the resulting spatial data, closing the loop between experimental design, automated execution and data-driven decisions about what to run next. This is an opportunity for a hybrid scientist who wants to help define how a self-driving biology laboratory works.

Main tasks and responsibilities:

• Design, build and validate automated liquid-handling workflows on an Opentrons Flex platform (Python API).
• Establish and run spatial proteomics on in vitro co-cultures, engineered 3D tumor models and tissue sections.
• Implement clonal barcoding and lineage tracing readouts, including in situ detection and debarcoding of individual clones.
• Scale perturbation experiments to high-throughput formats, integrating robotics, automated microscopy and sample tracking into reproducible end-to-end pipelines.
• Develop and apply computational pipelines for image processing, cell segmentation, debarcoding and spatial statistics.
• Close the experimental-computational loop, using analysis results to guide the design of the next round of automated experiments (design of experiments, active learning).
• Ensure reproducibility and FAIR data practices: version-controlled code, documented protocols, structured metadata and open sharing of tools.
• Present results at internal meetings and international conferences, and contribute to manuscript preparation.
• Apply for competitive postdoctoral fellowships and, later, for independent career awards, with full support from the group and from IBEC's grants office in preparing the proposals.
• Collaborate closely with experimental and computational colleagues within the group and with external partners, and co-supervise students working on related tasks.

Requirements for candidates:

• A PhD in life sciences, bioengineering, biotechnology, computational biology or a related discipline.
• Solid hands-on wet-lab experience (mammalian cell culture, molecular biology and/or imaging-based assays).
• Programming proficiency in Python and/or R.
• Demonstrated experience in the analysis of large imaging and/or omics datasets.
• Strong interest in laboratory automation and in making experiments scalable, traceable and reproducible.
• Ability to work independently, take initiative and solve complex problems across the experimental-computational interface.
• Willingness to work in a highly multidisciplinary environment.
• Competencies and skills needed: communication, teamwork, proactivity, commitment, integrity, time management, critical and analytical thinking, precision, and focus.
• Full professional proficiency in English (written and spoken).

Advantageous:

• Hands-on experience with liquid-handling robots (Opentrons Flex or OT-2, Hamilton, Tecan) and their Python APIs.
• Experience with spatial proteomics (e.G., iterative immunofluorescence, PhenoCycler/CODEX, imaging mass cytometry, MIBI) or with spatial transcriptomics.
• Experience with DNA barcoding, lineage tracing, CRISPR screens or other high-throughput perturbation approaches.
• Experience with quantitative image analysis and deep learning tools for segmentation and classification (e.G., Cellpose, napari, scikit-image, PyTorch).
• Experience with 3D culture systems, organoids or microfluidic devices.
• Familiarity with workflow managers (Nextflow, Snakemake), containers (Docker, Singularity) and high-performance computing environments.
• Interest in AI-assisted experimental design, self-driving laboratories or foundation models for biological data.
• A background in cancer biology or immunology.

We Offer:

• Number of available po