About Us:

In

GERO.AI , our mission is to decelerate human aging and develop innovative therapies for chronic conditions. We utilize physics-based foundational models trained on longitudinal data (e.g. UK Biobank). By extracting continuous biological latents (metabolic, immune, and biological age), we model health resilience and functional units far beyond the limitations of ICD-10 codes. Our genetics-driven causal AI pipeline integrates human genetics with multi-omics data to identify novel treatments and accelerate drug discovery.

Position Overview:

We're seeking a Statistical Geneticist, Computational Biologist, or Bioinformatics Scientist with expertise in deep learning applications for variant annotation and functional genomics. You'll advance our target discovery platform by working with AI models, multi-omics data, and large-scale population genetics datasets to identify therapeutic targets and speed up drug discovery. Key tasks include processing genetics (GWAS/PheWAS), building reliable pipelines; implementing state-of-the-art methods; and developing production-ready code. The ideal candidate has strong computational genomics skills and a passion for translating biological data into drug development insights.

Key Responsibilities:

• Data management & preprocessing:

  Handle tabular data, genetics data (plink format), GWAS sumstats, molQTL.

• Statistical Genetics: Conduct WGS/WES common and rare variant association studies, followed by post-GWAS integration using colocalization, mendelian randomization, and multi-omics (transcriptomics/proteomics) analysis.

Qualifications & Skills

Education:

MSc/PhD in Statistical Genetics, Bioinformatics, Biostatistics, Computer Science, or related quantitative field.

Experience:

• 3+ years (MSc) with relevant experience (or relevant doctoral research for PhD).
• Demonstrated hands-on experience analyzing large-scale datasets with statistical inference and machine learning.
• Relevant publications in top-tier journals.

Technical Skills:

• Advanced Proficiency in Python and R.
• Proficient in a Unix/Linux command-line environment.
• Practical experience with population genetics, GWAS and post-GWAS methods.
• HPC or cloud computing experience is necessary.
• Experience in training deep neural networks is a plus.

Personal Attributes:

A proactive problem-solver who thrives in fast-paced, autonomous environments and is eager to master evolving technologies.

What We Offer:

• Competitive Compensation:

How to Apply:

Interested candidates should submit a CV detailing their relevant experience, specific skills related to the requirements above, and suitability for the role to dreamjob+biojan26@gero.ai .