PhD Developing AI Tools to Crack the Molecular Basis of Growth and Stress Resilience in Plants

PhD Developing AI Tools to Crack the Molecular Basis of Growth and Stress Resilience in Plants

Join us in advancing the frontier of plant sciences and making a tangible impact on agricultural resilience through the power of machine learning!

 

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 (

)

,

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.

 

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

 

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

 

Challenge. Change. Impact!

 

Faculty Electrical Engineering, Mathematics and Computer Science 

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment - which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

 

 to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.

 

CropXR

CropXR

 

Conditions of employment 

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment