15 ott - Varese
1000scholars
Deep Learning Scientist Data Science Yinxiu Zhan Lab IEO - European Institute of Oncology Milan, Italy € 45.000 per year gross About the Project Accurate RNA-based detection of genetic variants has the potential to streamline molecular profiling by extracting multiple layers of information from a single experimental technique. Unlike approaches that require separate assays for DNA variant calling and transcriptomic readouts, RNA sequencing can simultaneously capture gene expression profiles and evidence of expressed mutations, features that are particularly relevant when studying tumor biology and treatment response. Within the PRIME project, the Fellow will focus on designing, implementing, and benchmarking a hybrid CNN-Vision Transformer (ViT) framework that operates directly on RNA-seq-derived data to enable robust detection of expressed mutations. The work will include model development and optimization, definition of evaluation strategies and benchmarks, and systematic comparison against baseline approaches to quantify performance, generalizability, and practical utility.
This effort contributes to PRIME's broader goal of improving prediction of response to immune checkpoint inhibitors through RNA-driven computational methods. Key Responsibilities * Develop and optimize deep learning architectures for RNA-seq–based variant calling * Adapt and extend DeepVariant-like frameworks for RNA-specific mutation detection * Implement CNN and Vision Transformer models for local and global sequencing feature extraction * Benchmark RNA-based variant calls against matched DNA-seq ground truth datasets * Design validation pipelines and performance metrics (precision, recall, F1-score) * Collaborate with bioinformatics and machine learning teams to integrate variant calls into downstream predictive models * Contribute to scientific publications and technical documentation Required CompetenciesProgramming & Data Analysis * Advanced proficiency in Python * Experience with scient
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AM International