AI-Driven Genomic Data Analysis Developer

Mikrogen

Turkey

On-site

TRY 350,000 - 520,000

Full time

14 days+
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Job summary

Mikrogen in Türkiye seeks a bioinformatician or software developer to join our team building an AI-powered genomic analysis platform. The role sits at the intersection of bioinformatics and artificial intelligence to process, analyze, and interpret large-scale genomic datasets.

You will design pipelines, ML models, and user-friendly tools to support clinical and research applications in human genetics. Collaboration with researchers, geneticists, and software engineers is essential, with a

Qualifications

  • MSc or PhD in Bioinformatics, Computational Biology, CS, Genetics, or related field.
  • Proficient in Python and/or R; experience with bioinformatics libraries and ML frameworks.
  • Strong knowledge of genomic data formats (VCF, FASTQ, BAM, BED, GTF/GFF).
  • Experience applying ML to biological datasets; version control and API development.

Responsibilities

  • Design and implement algorithms for processing and analyzing NGS and other genomic data.
  • Develop and apply AI/ML models for variant classification and prioritization.
  • Build and maintain automated bioinformatics workflows and pipelines.
  • Perform annotation, filtering, and interpretation of genomic variants.
  • Collaborate with researchers, geneticists, and software engineers on tool development.
  • Contribute to documentation, version control, and regulatory compliant bioinformatics components.

Skills

Python/R
Bioinformatics
ML frameworks
Version control
API development

Education

MSc or PhD in Bioinformatics/Computational Biology/CS

Tools

Biopython
scikit-learn
TensorFlow
Snakemake
Git

Job description

Mikrogen in Türkiye seeks a bioinformatician or software developer to join our team building an AI-powered genomic analysis platform. The role sits at the intersection of bioinformatics and artificial intelligence to process, analyze, and interpret large-scale genomic datasets.

You will design pipelines, ML models, and user-friendly tools to support clinical and research applications in human genetics. Collaboration with researchers, geneticists, and software engineers is essential, with a

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