Head of AI Core for Protein Engineering
Pubblicato il 23-10-2026 - Telethon in Campania
Fondazione Telethon (FT) is a leading biomedical charity committed to advancing research into tangible therapeutic options for people living with rare genetic diseases. Through its affiliated research institutes – TIGEM (Naples) and SR-TIGET (Milan) – FT has established an internationally recognized track record in gene and cell therapy, genome engineering, RNA-based therapeutics, and translational medicine.
To further accelerate the development of next-generation therapeutic platforms, FT is establishing a cross-institute Artificial Intelligence Core for Protein Engineering , aimed at supporting the design, optimization, and translational development of therapeutic proteins, genome-editing enzymes, regulatory proteins, and other biologically relevant macromolecules. The Core will serve as a strategic computational platform integrating advances in machine learning, protein science, and functional genomics across TIGEM and SR-TIGET.
THE POSITION
FT is seeking an experienced and forward-thinking scientist to serve as Head of the AI Core for Protein Engineering , leading an integrated initiative across TIGEM and SR-TIGET. The position offers a unique opportunity to shape a strategic enabling platform within the Fondazione Telethon R&D; ecosystem and contribute directly to the development of innovative therapeutic approaches.
The successful candidate will design, implement, and manage the AI Core, supporting translational research programs and enabling the application of state-of-the-art artificial intelligence methodologies to protein engineering challenges across the two Institutes.
The position will be based at TIGEM (Naples) and requires regular interactions and travel between the two sites.
Key Responsibilities
- Establish and lead the AI Core for Protein Engineering, ensuring alignment with the strategic priorities of the two Institutes
- Define and implement the Core’s scientific, methodological, and operational strategy, including workflows, data standards, benchmarking frameworks, and resource allocation.
- Evaluate, benchmark, and select state-of-the-art computational approaches for protein engineering, including protein language models, structure-based methods, generative AI approaches, and variant-effect prediction tools.
- Coordinate the application of selected methodologies to priority translational programs involving genome-editing systems, therapeutic proteins, RNA-binding proteins, gene regulation technologies, and other protein engineering initiatives.
- Develop and oversee reproducible computational workflows, data management procedures, quality standards, and reporting practices.
- Recruit, mentor, and supervise dedicated machine learning scientists, structural bioinformaticians, and trainees.
- Serve as the primary interface between computational and experimental groups, facilitating the translation of computational predictions into experimentally testable hypotheses.
- Coordinate interactions with external academic collaborators, industrial partners,
technology providers, and AI-focused organizations.
- Monitor emerging developments in artificial intelligence and computational biology and drive innovation activities within the Core.
- Contribute to grant applications, collaborative projects, and external funding initiatives.
Qualifications
- PhD in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Biophysics, Protein Engineering, Structural Biology, or a closely related discipline.
- At least 5 years of post-PhD experience in computational protein science, AI-driven biology, or related fields.
- Demonstrated expertise in one or more of the following areas:
- Protein language models;
- Variant-effect prediction;
- Deep mutational scanning analysis;
- Structure-based protein modeling;
- Protein design and engineering;
- Generative AI approaches for biological sequences;
- Molecular dynamics simulations and biophysical modeling of protein function;
- Prediction of protein–protein, protein–ligand, and protein–RNA interactions.
- Strong programming and computational skills, including experience with Python-based scientific ecosystems and machine learning frameworks.
- Experience with high-performance computing environments, GPU-based workflows, cloud computing, or large-scale model deployment.
- Proven leadership capabilities, including team building and management of multidisciplinary projects.
- Ability to translate complex computational concepts into clear and actionable insights for experimental scientists and translational researchers.
- Excellent command of written and spoken English.
- Outstanding communication, organizational, and sta