PhD in Genomics & AI for Plant Biology - Hybrid/Remote

Unist; Montpellier; Ibrido; EUR 21.000 - 30.000; Tempo pieno

Descrizione del lavoro

IRD Occitanie in Montpellier invites applications for a fixed-term doctoral contract within the DIADE unit to study plant genomics using genomic language models and graph learning. The project aims to predict gene expression in plants by linking cis-regulatory elements to expression profiles and evaluating advanced sequence models.

You will work under PI supervision and collaborate with international partners, using GPUs and open science practices.

Competenze

• Strong command of Python and a deep learning framework (PyTorch).
• Knowledge of machine learning and deep learning (Transformers, representation learning).
• Experience with graph learning (PyTorch Geometric, DGL) and language models (Hugging Face) is a plus.
• Knowledge of genomics and sequencing data handling (NGS formats, bioinformatics tools).
• Scientific English, written and spoken.

Mansioni

• Build a reference dataset linking cis-regulatory elements to spatio-temporal expression profiles in Arabidopsis and rice.
• Evaluate genomic language models, both general-purpose and plant-specific, for expression prediction. Compare with classical ML approaches.
• Design graph learning models (GNNs, Graph Transformers) that integrate sequence representations into gene regulatory networks.
• Interpret the models to identify key regulatory modules and assess transfer to other cereals.
• Disseminate results: publications and international conferences; write the PhD thesis.

Conoscenze

Python

Deep learning

Transformers

Graph learning

Bioinformatics

Machine learning

PyTorch Geometric

DGL

Hugging Face

NGS data handling

Formazione

Master's degree or engineering degree in bioinformatics, computer science, or computational biology

Strumenti

PyTorch

PyTorch Geometric

DGL

Hugging Face

Descrizione del lavoro

IRD Occitanie in Montpellier invites applications for a fixed-term doctoral contract within the DIADE unit to study plant genomics using genomic language models and graph learning. The project aims to predict gene expression in plants by linking cis-regulatory elements to expression profiles and evaluating advanced sequence models.

You will work under PI supervision and collaborate with international partners, using GPUs and open science practices.