JOB DESCRIPTION

We are seeking a highly motivated PhD candidate to join our interdisciplinary research team focused on enhancing plant resilience using cutting-edge data analysis and machine learning techniques. This exciting opportunity involves developing computational models to investigate growth-stress trade-offs in plants, with a focus on the structure, dynamics, and regulatory roles of protein complexes, particularly patterns of direct and indirect protein–protein interactions involved in signaling and transcriptional regulation.
The candidate will develop machine learning approaches to model how these complexes interact and influence gene expression under diverse stress conditions. Leveraging newly generated multi-omics datasets—including protein interaction profiles, DNA-binding assays, and (single-cell) transcriptomic data—the PhD candidate will explore hybrid modeling strategies that integrate mechanistic representations (e.g., dependency graphs) with data-driven methods (e.g., ensemble predictors).
These models will form the basis of a modular, mechanistic framework for identifying key regulators of growth-stress resilience and support experimental validation across the consortium. This position offers a unique opportunity to work at the interface of systems biology, plant science, and artificial intelligence.
The project is part of the Plant/Crop-XR program (cropxr.org/about-us/team/research), a highly collaborative 10-year national initiative involving universities and industry partners, with a mission to design resilient crops through data-driven strategies.

Key Challenges:

• Developing machine learning approaches that integrate partial biological knowledge with data-driven insights.
• Collaborating with computational modelers and experimental plant biologists to iteratively validate and refine models.
• Designing data-driven models to represent dynamic and compositional protein complexes.
• Integrating multi-omics datasets (e.g., RNA-seq, AP/MS, proximity ligation, GWAS) from various sub-projects to identify regulatory modules and interaction networks.
• Contributing to the development of hybrid models that combine mechanistic and machine learning approaches to simulate plant responses to environmental stress, and proposing “smart experiments” to iteratively improve model performance

Benefits:

• Work on cutting-edge research with real-world impact in agriculture and plant biology.
• Join a collaborative, interdisciplinary, and dynamic research environment.
• Access to state-of-the-art computational and laboratory facilities.
• Support for professional development and participation in international conferences.

To thrive as a PhD candidate, it’s crucial to have a strong research mindset driven by curiosity and passion for your topic. Reflecting on your motivation for pursuing a PhD trajectory is essential, as this path involves unique challenges and uncertainties inherent to scientific exploration. Success requires dedication, adaptability, the ability to analyze complex problems, manage your time effectively, innovate and stay resilient under pressure. Combined with the ability and willingness to work independently and collaborate well, these qualities are indispensable for a fulfilling PhD journey.
These experiences will build you as an independent researcher, expand your professional network, and pave the way for diverse career paths, inside or outside academia.

REQUIREMENTS:

• A Master’s degree in Computer Science, Artificial Intelligence, Computational Biology, Bioinformatics, or a related field, with an affinity for plant sciences.
• Strong background in machine learning and data analysis.
• Ability to work independently and as part of a multidisciplinary team.
• Experience with biological data integration and simulation modeling.
• Excellent programming skills (e.g., Python, R) and familiarity with machine learning libraries.
• Strong analytical thinking and problem-solving skills.
• Strong interpersonal communication and collaboration abilities
• Strong communication skills and proficiency in English.

Please refer the Job description for details