For the National Growth Fund (NGF) project “Groeien met Groen Staal” (GGS), a PhD position for the period of 4 years is available in the context of modelling green steel mechanics from the atomic scale. In this project, the University of Groningen will investigate micro-scale and atomic-scale processes of dislocation-precipitate interactions for multi-component alloy systems that are relevant for the new green steels compositions, where precipitates can occur due to impurities and tramp elements. The goal of the project is to devise a multi-scale framework where atomic-scale mechanisms of precipitate shearing/looping are incorporated into micro-scale framework of dislocation-precipitate interactions. This model will enable to predict precipitate strengthening as a function of the green steel composition.

The successful candidate will be supervised by prof. dr. Francesco Maresca, chair of the Mechanics of Materials unit of the Engineering and Technology institute Groningen (ENTEG).

We are looking for a motivated student to fill a fully funded PhD position at the University of Groningen. The aim of this PhD project is to develop novel therapies for healthcare through the engineering of smart bioelectronic devices. Although interfacing electronics with biological systems has shown great promise for a wide variety of disorders, personalized therapies that adapt in real-time have not been established yet. The development of soft and flexible bioelectronic devices that use feedback of integrated biosensors and high-density microelectrode arrays will enable precision therapy to improve healthcare.

Background

Bioelectronic medicine, or therapies based on electronic devices, is currently witnessing an explosion of interest in both academia and industry. Inspired by clinical successes such as the cardiac pacemaker, cochlear implants, and deep brain stimulators, bioelectronic medicine has the potential to revolutionize healthcare because it offers local and targeted treatments with reduced side effects and improved outcomes. For these therapies to be effective and deliver personalized therapies, devices need to record and process relevant biological data and adapt therapeutic parameters real-time.

In this project, you will develop low-power biosensors and circuits that can record and processes biological signals. This will be used as real-time feedback to deliver targeted electric fields with high spatial resolution with optimized stimulation parameters for precise and safe modulation.

To improve the performance and integration of bioelectronic devices with biology, devices will be made of organic materials (polymers) to better match the mechanical properties of cells and tissue. This technology will be applied on rigid substrates for lab-on-chip applications as well as on flexible substrates for in vivo applications with a strong focus on reliability and stable performance to realize clinical translation.

This PhD project integrates microbial electrochemical synthesis of fatty acids for the efficient and stable production of fit-for-purpose bioplastics. The world is becoming more polluted with plastics from fossil origin. Luckily, biologically produced and degradable alternatives from residual biomass are offering a solution. PHA, a polymer consisting of fatty acids, can replace conventional plastics. The current production process is not efficient enough, and the input biomass does not necessarily give the desired plastic product and properties. Microbial electrosynthesis offers the opportunity to produce fatty acids from CO2 and renewable electricity. E-plastic aims to integrate fatty acid electrosynthesis into the PHA production process.

Insufficient and poor-quality sleep are major and growing societal concerns, strongly linked to adverse mental health outcomes such as anxiety, depression, and neurodegenerative disease. Beyond general health effects, sleep plays a critical role in memory processing by consolidating experiences while reducing their emotional intensity. When sleep is disrupted especially after fearful events, this process fails and leads to heightened distress during memory recall and potentially contributing to the development of maladaptive or traumatic memories. In this project, we aim to elucidate the neurobiological processes to contribute to the development of maladaptive/traumatic memories. 

Researchers have developed increasingly powerful foundation models, including transformers and diffusion models, that demonstrate remarkable capabilities across a wide range of tasks. The next decade will focus on transforming these models into autonomous, long-horizon agentic AI systems, such as LLM-based multi-agent systems and embodied agents, that can operate robustly in complex, real-world environments. Unlike static foundation models, agentic AI systems are designed to perceive their surroundings, formulate and execute plans, take goal-directed actions, and continuously learn from interaction.

We are seeking a highly motivated PhD candidate to conduct research on agentic AI systems, with the goal of developing a deeper conceptual and theoretical understanding of their underlying mechanisms. This position will focus on advancing the planning and reasoning capabilities, safety, and continual learning mechanisms of agentic AI systems. Depending on the candidate’s interests, there will also be opportunities to engage in international collaborations and explore real-world applications of agentic AI systems across diverse domains.