PhD Position: The Cellular Basis of Plant Regeneration

Plants have the fascinating ability to regenerate from a single cell or a damaged tissue. This ability requires extensive changes in, amongst other things, cell programming and patterning. Current methods for hormone-induced regeneration in tissue culture often give limited and unpredictable results. Even in optimal conditions only a few cells or groups of cells make the switch to regeneration and yet fewer manage to form the organised cell patterns of a new shoot or root. Cell and tissue polarity guide, for example, local accumulation of molecules and cell division patterns, which are essential processes during regeneration. In this PhD project, you will combine studies on cell polarity, cell division and plant hormones to investigate how cellular patterns and signals help specific cells to switch to regeneration and be successful.

Reliable and reproducible plant regeneration is a key factor for successful propagation of many crops, yet detailed insights in their regeneration processes are limited. In this project, you will use tomato as a main model species, supplemented with work in Arabidopsis thaliana.

You will use molecular techniques, cell biology, tissue culture and advanced imaging to study cellular processes during regeneration.

You will assist in teaching the new Crop Biotechnology and Engineering (CBE) MSc programme, for example by supervising tutorials or assisting during practicals. This position has a teaching load of up to 10% of your working time.

Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate.

PhD Position: The Cellular Basis of Plant Regeneration

Plants have the fascinating ability to regenerate from a single cell or a damaged tissue. This ability requires extensive changes in, amongst other things, cell programming and patterning. Current methods for hormone-induced regeneration in tissue culture often give limited and unpredictable results. Even in optimal conditions only a few cells or groups of cells make the switch to regeneration and yet fewer manage to form the organised cell patterns of a new shoot or root. Cell and tissue polarity guide, for example, local accumulation of molecules and cell division patterns, which are essential processes during regeneration. In this PhD project, you will combine studies on cell polarity, cell division and plant hormones to investigate how cellular patterns and signals help specific cells to switch to regeneration and be successful.

Reliable and reproducible plant regeneration is a key factor for successful propagation of many crops, yet detailed insights in their regeneration processes are limited. In this project, you will use tomato as a main model species, supplemented with work in Arabidopsis thaliana.

You will use molecular techniques, cell biology, tissue culture and advanced imaging to study cellular processes during regeneration.

You will assist in teaching the new Crop Biotechnology and Engineering (CBE) MSc programme, for example by supervising tutorials or assisting during practicals. This position has a teaching load of up to 10% of your working time.

Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate.

PhD Position: The Cellular Basis of Plant Regeneration

Plants have the fascinating ability to regenerate from a single cell or a damaged tissue. This ability requires extensive changes in, amongst other things, cell programming and patterning. Current methods for hormone-induced regeneration in tissue culture often give limited and unpredictable results. Even in optimal conditions only a few cells or groups of cells make the switch to regeneration and yet fewer manage to form the organised cell patterns of a new shoot or root. Cell and tissue polarity guide, for example, local accumulation of molecules and cell division patterns, which are essential processes during regeneration. In this PhD project, you will combine studies on cell polarity, cell division and plant hormones to investigate how cellular patterns and signals help specific cells to switch to regeneration and be successful.

Reliable and reproducible plant regeneration is a key factor for successful propagation of many crops, yet detailed insights in their regeneration processes are limited. In this project, you will use tomato as a main model species, supplemented with work in Arabidopsis thaliana.

You will use molecular techniques, cell biology, tissue culture and advanced imaging to study cellular processes during regeneration.

You will assist in teaching the new Crop Biotechnology and Engineering (CBE) MSc programme, for example by supervising tutorials or assisting during practicals. This position has a teaching load of up to 10% of your working time.

Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate.

PhD Position: Designing Virtual Reality Biofeedback for Enhanced Emotion Regulation in Police and Youth

In the NWO National Science Agenda-funded CONTEXT project, researchers from neuroscience, developmental psychology and forensic psychiatry collaborate with societal stakeholders, including youth organisations and police, to improve emotion regulation at moments when it is needed most.

The project is a unique collaboration between eight academic institutions in the Netherlands and more than twenty societal partners, representing fields such as security, education, healthcare and social services. The CONTEXT project will develop personalised biofeedback methods that train youth and police to recognise subtle, often unconscious, signals of stress. It will enable target groups to react more adequately in stressful moments by improving the early recognition of these signals in daily life and by training specific context-appropriate emotion-control techniques for stressful situations through virtual reality. By preventing violent escalations, CONTEXT aims to enhance societal safety and resilience while fostering stronger connections between societal groups that often experience high-impact conflict.

As a PhD candidate, you will help further refine and validate our virtual reality biofeedback game for both youth and police. You will collaborate with stakeholders, end-users, game designers and our CONTEXT science team to improve our virtual reality game and its biofeedback algorithms. You will perform user-interaction tests in target groups and record and analyse psychophysiological measures of autonomic nervous system balance during acute stress such as heart rate variability. You will help optimise our biofeedback algorithms and, after small N assessments, contribute towards setting up a large-scale trial to assess the efficacy of the newly developed methods. For this you will interact extensively with other work packages in the project that focus on neuroimaging, ecological momentary and physiological assessments, theory and ethics, as well as societal impact.

You will have the opportunity to make a start with your teaching portfolio and dedicate 10% of your working time to teaching in the psychology curriculum.

Would you like to learn more about what it’s like to pursue a PhD at Radboud University? Visit the page about working as a PhD candidate.

Postdoc Position: AI-based Load Forecasting for Energy Systems

The energy transition is one of the most urgent challenges of our time. The electricity grid is under increasing pressure due to electrification, renewable energy growth and rising demand, and AI is key to solving it. As a Postdoctoral Researcher in AI-based Load Forecasting at Radboud University, you will be at the heart of this challenge. You will develop cutting-edge AI models that predict electricity consumption at both grid and individual building level, directly contributing to reducing network congestion in the Netherlands. Your work will have real-world impact: the models you develop will be tested and validated together with industry partners such as Alliander and Stedin and made openly available to the broader AI and energy community.

You will conduct research on advanced AI methods for short-term load forecasting as part of the FlexLab AI Innovation Lab, a collaboration between Radboud University, Alliander, Stedin, the Netherlands Organisation for Applied Scientific Research (TNO), Eindhoven University of Technology (TU/e), HAN University of Applied Sciences and several SME partners. FlexLab develops, tests and validates AI technologies for flexible energy management with the goal of tackling network congestion in medium- and low-voltage grids. Radboud University leads the load forecasting work package (WP4), and you will be the key researcher driving this work forward.

Your research will focus on developing and validating AI models for load forecasting in scenarios where current models fall short, such as extreme weather events, grid incidents and high variability in renewable energy. You will explore techniques including graph neural networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty quantification in forecasting and integrate your models into the open-source OpenSTEF platform and the Linux Foundation Energy ecosystem.

You will work closely with SME partners in short-cycle innovation trajectories of 6 to 18 months, translating scientific advances into practical prototypes to be tested in real or simulated environments. You will contribute to scientific publications, open-source releases and knowledge-sharing events with the broader energy and AI community.