h3Deep Learning Scientist Data Science Yinxiu Zhan Lab /h3pIEO - European Institute of Oncology /ppMilan, Italy /pp€ 45.000 per year gross /ph3About the Project /h3pAccurate 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. /ppWithin 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. /ph3Key Responsibilities /h3ulliDevelop and optimize deep learning architectures for RNA-seq–based variant calling /liliAdapt and extend DeepVariant-like frameworks for RNA-specific mutation detection /liliImplement CNN and Vision Transformer models for local and global sequencing feature extraction /liliBenchmark RNA-based variant calls against matched DNA-seq ground truth datasets /liliDesign validation pipelines and performance metrics (precision, recall, F1-score) /liliCollaborate with bioinformatics and machine learning teams to integrate variant calls into downstream predictive models /liliContribute to scientific publications and technical documentation /li /ulh3Required Competencies /h3h3Programming Data Analysis /h3ulliAdvanced proficiency in Python /liliExperience with scientific computing libraries (numpy, pandas, scipy) /liliFamiliarity with Linux-based HPC environments /li /ulh3Machine Learning Deep Learning /h3ulliStrong experience with deep learning frameworks (PyTorch or TensorFlow) /liliSolid understanding of CNNs and transformer-based architectures /liliExperience working with sequencing data representations /li /ulh3Genomics Bioinformatics /h3ulliExperience in next-generation sequencing (RNA-seq, DNA-seq) /liliExperience with read alignment, variant calling, and quality control pipelines /liliFamiliarity with tools such as STAR, GATK, samtools, bcftools /li /ulh3Soft Skills /h3ulliStrong analytical and problem-solving skills /liliAbility to work independently and within interdisciplinary teams /liliClear communication skills and attention to reproducibility /li /ulh3Desirable Qualifications /h3ulliExperience with DeepVariant or similar variant calling frameworks /liliFamiliarity with FFPE sequencing data /liliBackground in cancer genomics or immuno-oncology /li /ulh3Educational Requirements /h3ulliPhD or Master’s degree in Mathematics, Physics, Bioinformatics, Computational Biology, Computer Science, or related fields /liliDemonstrated research experience in deep learning /li /ulp1 day left /p #J-18808-Ljbffr

📌 Deep Learning Scientist Data Science Yinxiu Zhan Lab (Milano)
🏢 1000scholars
📍 Milano