IEO - European Institute of Oncology
Milan, Italy
€ ****** 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 Competencies
Programming & Data Analysis
Advanced proficiency in Python
Experience with scientific computing libraries (numpy, pandas, scipy)
Familiarity with Linux-based HPC environments
Machine Learning & Deep Learning
Strong experience with deep learning frameworks (PyTorch or TensorFlow)
Solid understanding of CNNs and transformer-based architectures
Experience working with sequencing data representations
Genomics & Bioinformatics
Experience in next-generation sequencing (RNA-seq, DNA-seq)
Experience with read alignment, variant calling, and quality control pipelines
Familiarity with tools such as STAR, GATK, samtools, bcftools
Soft Skills
Strong analytical and problem-solving skills
Ability to work independently and within interdisciplinary teams
Clear communication skills and attention to reproducibility
Desirable Qualifications
Experience with DeepVariant or similar variant calling frameworks
Familiarity with FFPE sequencing data
Background in cancer genomics or immuno-oncology
Educational Requirements
PhD or Master's degree in Mathematics, Physics, Bioinformatics, Computational Biology, Computer Science, or related fields
Demonstrated research experience in deep learning

📌 Deep Learning Scientist Data Science Yinxiu Zhan Lab (Milano)
🏢 IEO European Institute of Oncology
📍 Milano