Deep Learning Scientist Data Science Yinxiu Zhan LabIEO - European Institute of OncologyMilan, Italy€ 45.000 per year grossAbout the ProjectAccurate 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 ResponsibilitiesDevelop and optimize deep learning architectures for RNA-seq–based variant callingAdapt and extend DeepVariant-like frameworks for RNA-specific mutation detectionImplement CNN and Vision Transformer models for local and global sequencing feature extractionBenchmark RNA-based variant calls against matched DNA-seq ground truth datasetsDesign validation pipelines and performance metrics (precision, recall, F1-score)Collaborate with bioinformatics and machine learning teams to integrate variant calls into downstream predictive modelsContribute to scientific publications and technical documentationRequired CompetenciesProgramming & Data AnalysisAdvanced proficiency in PythonExperience with scientific computing libraries (numpy, pandas, scipy)Familiarity with Linux-based HPC environmentsMachine Learning & Deep LearningStrong experience with deep learning frameworks (PyTorch or TensorFlow)Solid understanding of CNNs and transformer-based architecturesExperience working with sequencing data representationsGenomics & BioinformaticsExperience in next-generation sequencing (RNA-seq, DNA-seq)Experience with read alignment, variant calling, and quality control pipelinesFamiliarity with tools such as STAR, GATK, samtools, bcftoolsSoft SkillsStrong analytical and problem-solving skillsAbility to work independently and within interdisciplinary teamsClear communication skills and attention to reproducibilityDesirable QualificationsExperience with DeepVariant or similar variant calling frameworksFamiliarity with FFPE sequencing dataBackground in cancer genomics or immuno-oncologyEducational RequirementsPhD or Master’s degree in Mathematics, Physics, Bioinformatics, Computational Biology, Computer Science, or related fieldsDemonstrated research experience in deep learning1 day left#J-18808-Ljbffr

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