AI-Driven Genomic Data Analysis Developer

Mikrogen; Turkey; Presencial; TRY 350.000 - 520.000; Jornada completa

Descripción de la vacante

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

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

Python/R

Bioinformatics

ML frameworks

Version control

API development

Educación

MSc or PhD in Bioinformatics/Computational Biology/CS

Herramientas

Biopython

scikit-learn

TensorFlow

Snakemake

Git

Descripción del empleo

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