Organisation / Company: Universidad Politécnica de Madrid, Department CBGP Centro de Biotecnología y Genómica de Plantas UPM-INIA/CSIC
Research Focus
Current breeding and disease-resistance prediction models often treat biological interactions as static linear components. This project aims to disrupt this paradigm by leveraging Foundation Models and Deep Learning to capture non-linear dynamics in agricultural systems. The selected fellow will work at the interface of Computational Biology and AI, focusing on:
* Sequence-Based Foundation Models: Utilizing self-supervised learning to extract transferable features from raw genomic data (DNA/RNA) without relying on pre-defined marker sets.
* High-Dimensional Phenomics: Deploying Computer Vision architectures (e.g., Vision Transformers) to quantify complex disease phenotypes from high-throughput imaging.
* Multimodal Integration: Designing fusion architectures that integrate heterogeneous data streams, genomics, imaging, and environmental factors, into a unified predictive engine.
The fellowship will generate reusable models and open resources, with direct relevance for sustainable crop health and European agri-food resilience.
Research team/group
The Rocinante Lab (CBGP-UPM, Madrid) develops AI-enabled predictive breeding tools to support sustainable crop protection. We combine large host–pathogen datasets, curated high-throughput image repositories, institutional HPC (SLURM) and GPU workstations, and strong European/industry networks. The fellow will benefit from structured mentoring, opportunities for collaboration/secondments, and an open-science environment (code, models, and datasets released when possible).
Eligibility Requirements
* PhD Rule: Must hold a PhD at the call deadline (Sept 9, 2026).
* Experience Rule: Normally up to 8 years full-time research experience post-PhD.
* Mobility Rule: Must not have resided or worked in Spain for more than 12 months in the 36 months prior to the deadline.
Qualifications
* Proficiency in Python and deep learning frameworks (PyTorch/TensorFlow).
* Experience or a strong interest in Genomic Prediction or LLMs/Transformers.
* A drive to publish in high-impact journals and transition to scientific independence.
Please send your CV and a brief motivation letter to Dr. Julio Isidro Sanchez ( ).
Subject: MSCA 2026 EoI - [YourNameandLastName]
Deadline for Expression of Interest: April – 30 – 2026.
Supervisor's Name: Dr. Julio Isidro Sanchez #J-18808-Ljbffr