Postdoctoral Researcher

About the group

The successful candidate will collaborate closely with the FinnGen consortium, one of the world’s largest genomic research initiatives, and contribute to a vibrant, international team with access to unique datasets and world-class computational resources.

The Computational and Statistical Genomics group at FIMM, led by Simone Rubinacci, develops methods for large-scale human genomics and statistical genetics. Our work spans algorithm development, cloud computing, and population-scale sequencing to understand human disease and genetic variation better.

What you will work on

This role sits at the core of our data infrastructure. Your primary focus will be on ensuring that our WGS and WES data are clean, harmonized, and analysis-ready. Over time, you will also have the opportunity to contribute to downstream genomic analyses and methods work.

• Processing, quality control, and harmonization of large-scale Whole Genome and Whole Exome Sequencing datasets.
• Building and maintaining scalable, reproducible workflows on HPC and cloud platforms, including Google Cloud.
• Variant calling and QC for SNVs, indels, and structural variants.
• Collaborating with FinnGen network researchers and international partners on joint analyses.
• Over time: contributing to haplotype phasing, imputation, population genetics analyses, and integration of long-read sequencing and pangenome approaches.

What we are looking for

We welcome candidates from bioinformatics, computational biology, statistical genetics, computer science, or any related quantitative field. A PhD is preferred for the postdoctoral level, but strong candidates with equivalent research or industry experience are very welcome to apply.

Core skills (the focus of this role)

• Processing and analyzing WGS and/or WES data, including alignment, duplicate marking, and base quality recalibration (e.g. using BWA, GATK, Samtools).
• Variant calling and quality control for SNVs and indels; familiarity with common QC metrics such as coverage, Ti/Tv ratios, and contamination checks.
• Writing and maintaining reproducible analysis pipelines using workflow managers such as Nextflow, Snakemake, or WDL/Cromwell.
• Comfortable working in Linux/HPC environments with job schedulers (SLURM or similar) and shell scripting.
• Scripting in Python and/or R for data processing, QC visualization, and analysis.
• Experience with version control (Git) and containerization tools (Docker or Singularity) for reproducible environments.

Useful experience (good to have, not required)

• Cloud computing, particularly Google Cloud (GCP); experience with cloud storage and scalable compute is a plus.
• Structural variant calling and quality control (e.g. using Manta, PBSV, or similar tools).
• Haplotype phasing or genotype imputation (e.g. SHAPEIT, Beagle, or Michigan Imputation Server).
• Population genetics concepts such as population stratification, relatedness inference, or GWAS.
• Exposure to long-read sequencing data (ONT or PacBio) or emerging pangenome reference approaches.
• Familiarity with large-scale genomic databases or biobank data (e.g. FinnGen, UK Biobank, gnomAD).
• Knowledge of data governance and security practices for controlled-access human genomic data.

What We Offer

• Salary: €4,500–5,500/month gross, in accordance with the University of Helsinki salary system (YPJ), based on qualifications, experience, and performance.
• Contract: Fixed-term for two years, with the possibility of extension depending on funding and performance.
• Research environment: Access to FinnGen’s and other large omics data sources, the Finnish IT Center for Science (CSC), and extensive Google Cloud infrastructure.
• Career development: You will have the opportunity to contribute to high-impact publications, present at international conferences, and develop expertise at the intersection of genomics infrastructure and large-scale data analysis.
• Mentorship: Close day-to-day collaboration with Simone Rubinacci and the broader group, with regular feedback and support for your scientific and professional growth.
• Location: FIMM is based at the Meilahti medical campus in Helsinki, one of Europe’s most livable cities, within a dynamic and international research community.