AI-Based Genetic Data Analysis Software Developer / Bioinformatician

Organisation/Company Mikrogen Research Field Computer science » Programming Researcher Profile Established Researcher (R3) Positions Other Positions Application Deadline 10 Oct 2026 - 02:06 (Europe/Istanbul) Country Türkiye Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

We are seeking a highly motivated and skilled bioinformatician or software developer to join our team in building an AI-powered genomic analysis platform. The ideal candidate will work at the intersection of bioinformatics and artificial intelligence to develop robust, scalable solutions for processing, analyzing, and interpreting large-scale genomic datasets. You will contribute to the design and implementation of pipelines, machine learning models, and user-friendly tools to support clinical and research applications in human genetics.

Key Responsibilities:

• Design and implement algorithms for processing and analyzing next-generation sequencing (NGS) and other genomic data (e.g. SNP, gene panels)
• Develop and apply AI/ML models for tasks such as variant classification, pattern recognition, and prioritization of genetic findings
• Build and maintain automated bioinformatics workflows and pipelines
• Perform annotation, filtering, and interpretation of genomic variants
• Collaborate with researchers, geneticists, and software engineers on the development and validation of genomic analysis tools
• Contribute to the documentation, version control, and compliance of bioinformatics components with regulatory standards (e.g. ISO, CLIA/CAP)

Required Qualifications:

• Degree (MSc or PhD) in Bioinformatics, Computational Biology, Computer Science, Genetics, or a related field
• Strong programming skills in Python and/or R, with experience using bioinformatics libraries and ML frameworks (e.g. Biopython, scikit-learn, TensorFlow, pandas, Snakemake)
• Solid understanding of genomic data formats (e.g. VCF, FASTQ, BAM, BED, GTF/GFF)
• Experience in applying machine learning to biological datasets is a strong advantage
• Familiarity with version control systems (e.g. Git) and API development
• Strong analytical skills, attention to detail, and ability to work in a multidisciplinary team

Preferred Qualifications:

• Experience with clinical genetics applications (e.g. carrier screening, PGT, rare disease diagnostics)
• Knowledge of genomic variant interpretation standards and tools (e.g. ClinVar, OMIM, ACMG guidelines)
• Familiarity with secure data handling, HIPAA/GDPR compliance, and cloud-based platforms (AWS, GCP)
• Background in biobank data integration or multi-omics analytics is a plus