Julian Vogt

Leading computational analysis for multi-omics projects in developmental biology. Designing and maintaining Nextflow-based pipelines for bulk and single-cell RNA-Seq. Collaborating with experimental groups to interpret complex genomic datasets.

Developed machine learning models for cancer subtype classification using multi-omics data. Implemented variant calling pipelines for whole-genome sequencing of tumor samples. Published 4 first-author papers in peer-reviewed journals.

Built automated QC pipelines for NGS data processing. Developed custom tools for metagenomic classification and antimicrobial resistance gene detection. Improved processing throughput by 40% through workflow optimization.

“Machine Learning Approaches for Multi-Omics Integration in Cancer Subtype Classification”

Developed novel ensemble methods combining genomic, transcriptomic, and epigenomic data. Thesis supervised by Prof. Dr. Roland Eils. Graduated summa cum laude.

“Comparative Analysis of De Novo Genome Assembly Strategies for Non-Model Organisms”

Specialized in algorithms for sequence analysis and statistical genomics. Master's thesis focused on hybrid assembly approaches using long and short reads.

“In Silico Prediction of Antimicrobial Peptides Using Feature-Based Classification”

Minor in Computer Science. Early exposure to bioinformatics through undergraduate research in computational structural biology.