We are recruiting a highly motivated Deep Learning Scientist Fellow to join Yinxiu Zhan’s lab at the European Institute of Oncology (IEO) in Milan. The position is funded by the FIS (Italian Fund for Science) scheme of the Italian Ministry of University and Research (MUR), within the PRIME project (Predicting Response using Integrated Machine Learning and Expression data). The Scientist will develop and benchmark a hybrid CNN–Vision Transformer framework to detect expressed mutations directly from RNA-sequencing data.
Project Title: RNAseq-Driven Machine Learning framework for Predicting Immunotherapy Response
Project Code: FIS-2024-03049
CUP: J53C25002110001
Expected Start date of the contract: between June 2026 and September 2026
Contract type and duration: CCNL sanita’ privata, 4-years contract, potentially renewable
Gross salary: 45.000 Euro, inclusive of full social security contribution
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
Application deadlines and modalities:
Applications for participation in the selection must be received no later than 12.00 noon on April 11th, 2026. Applications must be sent through the appropriate link (APPLY Button)
The application must contain:
- a detailed and updated CV, dated and signed, certifying training and professional activities, with specific authorization to process personal data pursuant to art. 13 of GDPR 679/16;
- a cover letter outlining your research interests;
- any document or declaration useful for the assessment by the Examination Board;
- name and contact details of two/three references familiar with your previous work.
The European Institute of Oncology accepts no responsibility for applications not received by the deadline; the official date of receipt shall be the date on which the application is actually received.
Only applications that are complete, include all required documentation, and are submitted according to the indicated procedure will be considered.
Selection and evaluation procedure:
The selection of the successful applicants will take place through a comparative evaluation of the candidates based on qualifications and, if considered appropriate, an oral interview. The assessment will include an evaluation of the candidate's curriculum vitae, aimed at verifying the knowledge and skills required for the activities planned in the project.
The oral interview, if conducted, will be aimed at further examining the candidate's professional experience, personal motivation and aptitude, as well as to assess the candidate's availability for the multidisciplinary activities required for the project.
Failure to attend the interview, regardless the reason, will be considered as a withdrawal from the selection process.
Candidate evaluations will be conducted solely for the technical and discretionary purposes of identifying the most suitable candidate for the collaborative relationship covered by this announcement, in accordance with the private autonomy of the European Institute of Oncology pursuant to the Italian Civil Code.
These evaluations will not oblige the European Institute of Oncology to recruit individuals ranked appropriately on the ranking list. Participation in this procedure, therefore, will not entitle the first candidate on the ranking list to be hired.
Processing of personal data:
The personal data, compulsorily provided, will be processed in compliance with Legislative Decree no. 101 of 10 August 2018, which adapts the Personal Data Protection Code (Legislative Decree no. 196 of 30 June 2003) to the provisions of Regulation (EU) 2016/679, and only for the fulfilments related to this procedure and for those consequent to the possibleestablishment of the working relationship, according to what is also provided for in the privacy policy, which must be accepted by each candidate at the time of sending his/her application.
Equal opportunities:
This notice is issued in accordance with the principles of equal opportunity and non-discrimination in employment and remuneration, with particular regard to equality between men and women, pursuant to Legislative Decree No. 198/2006.
Posted on March 12th, 2026