15 PhD Degree-Fully Funded at Leiden University, Netherlands

As a PhD student, you will be embedded in the research groups Computational Imaging and Deep Learning (CIDL) and Applied Quantum Algorithms (aQa), part of the Leiden Institute of Advanced Computer Science (LIACS). The CIDL group develops cutting-edge techniques for advanced computational imaging systems, combining expertise from Mathematics, Computer Science, and Physics. The aQa group studies quantum algorithms and their applications to problems in natural sciences (physics, chemistry) and practical computing (machine learning, AI, optimization), and methods to make them compatible with near-term quantum devices.

Your project, “Deep learning for Electron Microscopy pipelines (DEEM), focuses on the application of deep learning methodologies to enhance electron microscopy (EM). The research addresses existing challenges within EM, such as image noise, artifacts, the optimization of acquisition parameters, and the integration of information from various EM techniques. The overall goal is to improve the output of EM systems and potentially extend their experimental capabilities by integrating deep learning throughout the imaging workflow, from data acquisition to final analysis.

We are hiring a PhD candidate to join our ERC-funded team on chronomicrobiology, a new field that bridges microbiology and chronobiology. The MicroClock program follows up on our novel discovery of a circadian clock in Bacillus subtilis. This PhD opportunity focuses on uncovering the molecular details of the bacterial circadian clock during interaction with the model plant, Arabidopsis thaliana. The PhD candidate will employ molecular biology tools to follow circadian gene expression in Bacillus subtilis as it colonizes the plant root. This work will be carried out in collaboration with our consortium partners at the John Innes Centre and LMU Munich. The PhD candidate will learn chronobiological principles and experimentation and be a key part of discovering a cross kingdom circadian system. The PhD candidate will be involved in an interdisciplinary, innovative project in a productive scientific environment with a good work-life balance.

Secure Computation Technologies, such as Multiparty Computation, allow the purposeful processing of private data (distilling value from such data), without compromising the privacy of this data. Today’s interconnected world, smart applications, and global business, necessitating the use of collaborative analytics, require the collection and processing of private information. In this PhD trajectory you will be exploring ways and developing protocols and primitives that enhance the security, functionality, and efficiency of secure computation technologies (e.g., multiparty computation – MPC), when designed for particular application scenarios, such as private machine learning use-cases. 

Carbohydrate metabolism is closely associated with health and many diseases and relies mainly on glycosidases and glycosyltransferases: carbohydrate processing enzymes that break and create glycosidic bonds, respectively. Controlling their activities is vital for understanding pathological processes, and developing therapeutics for glycan- related diseases. Recently, new targeted protein degradation (TPD) technologies, which disable disease-related proteins by degradation, have opened therapeutic possibilities for proteins that are otherwise hard to inhibit. While TPD within the cytoplasm, cell membrane, and extracellular space can be achieved with current approaches, proteins in the Golgi apparatus can not be degraded by means of the current methods. SWEET DEGRADATION aims to develop strategies that allow for the first time the degradation of proteins located in the Golgi apparatus, opening ways for a new therapeutic modality. Recent contributions of our research group in the field include Chem. Sci. 2024, 15, 15212-15220, Chem. Sci. 2023 14, 13581-13586 and J. Am. Chem. Soc. 2022 144, 14819-14827.

If you are passionate about pursuing a career in chemical biology and targeted protein degradation, and you are eager to be trained at one of the leading European institutes in chemical biology, this innovative project offers the perfect opportunity for you.

Vocal communication is a fundamental aspect of social interactions for a wide variety of species. In particular, humans and songbirds have evolved specialized neural circuits to finely perceive and adaptably produce a variety of vocal signals. This project aims to increase our understanding of how these circuits and vocal behavior are shaped by social experience. To do so, we will employ state-of-the art electrophysiological and behavioral recording methods as birds freely engage in dynamic vocal interactions. This neuroethological approach will reveal how complex streams of acoustic input are processed to produce context-dependent vocal responses and socially coordinated behavior. The successful candidate will carry out this work as part of a diverse international research team, supervised by Dr. Jonathan Benichov.

Are you eager to contribute to cutting-edge research at the intersection of AI and network science/social network analysis in a vibrant academic environment? Leiden University invites applications for a PhD position in AI for Network Analysis, starting as early as September 1, 2025.

We are looking for a motivated and talented early-career researcher with a strong interest in social network analysis, complex networks, or network science, and relevant background knowledge on methods in machine learning and AI. The successful candidate will focus on innovating the field of network analysis with AI methods. Examples of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data. The candidate is furthermore encouraged to propose their own research direction related to AI methods for advancing network analysis.

The opening is for a research position within the field of mathematical or theoretical biology, computational physics, applied mathematics or computational science. This PhD project is part of the Gravitation programme GreenTE (Green Tissue Engineering), a multidisciplinary consortium of 7 Dutch universities. Together, GreenTE will unravel how plants sense and respond to mechanical stimuli. These fundamental insights will be the basis to develop engineering interventions to improve plant regeneration, seed longevity and defenses against disease. The successful applicant will be an integral member of the GreenTE community, which offers an open, diverse and inspiring environment to engage in multidisciplinary mechanobiology research at the intersection of biology, chemistry and physics, and is expected to participate in GreenTE events, training and collaborations.

For a successful energy transition wherein green hydrogen is synthesized and consumed at a large scale, alternatives to poly- and per-fluoroalkyl membranes are necessary. Atomically thin 2D materials such as functionalized graphene have been identified as potential candidates for selective proton transport.
The project explores graphene as a working electrode and uses voltammetry techniques to establish correlations between the chemistry of the electrochemical active site and impact on transmembrane proton translocation.

The project aims to develop new methods and devices routed on the chemistry of 2D materials and the functionalization of graphene to store chemical energy and drive chemical reactions in fuel cells and electrolyzers.

The PhD students will be supervised by Grégory Schneider and Dennis Hetterscheid (Leiden Institute of Chemistry), and work in close collaboration with the teams at the Max Planck Institute of Microstructure Physics (Prof. Xinliang Feng) and at the Dresden University of Technology (Prof. Thomas Heine).

Do you want to study the flow of electrons and vibrations on ultrafast timescales (ps to ns) in materials that are very relevant to the energy-transition? Are you looking for a challenging project involving semiconductor synthesis and advanced ultrafast laser spectroscopy? Would you like to work in a friendly and collaborative environment? Then you might want to join our young team as a PhD candidate!

Project 1: Towards robust crisis detection and response mechanisms
It is, of course, best to nip a crisis in the bud. When a looming crisis is in its earliest stages of development, it may be still possible to intervene with relatively low costs and high chances of success. Unfortunately, it is not always easy to recognize an unfolding crisis. The list of organizations and governments that somehow missed a crisis is very long.

This PhD project seeks to uncover success factors. The PhD student will study how organizations create systemic cultures of alertness that help to detect signals of emerging threats. The project will build on the theory of high reliability organisations (HROs). This project will explore whether HRO principles help to detect crises in a timely way. It will focus on the recognition and handling of so-called anomalous events. It will study HROs that routinely deal with the challenge of threat recognition in the three threat domains (e.g., public order disturbances, pandemics, and extreme weather).

Project 2: Towards high-performing crisis response networks
When government actors initiate a crisis response, it is important to identify problems, formulate solutions, and ensure their implementation in a timely and acceptable fashion. The Covid-19 crisis demonstrated just how challenging this task can be. The crisis literature suggests that four types of activities are key to effective problem-solving in times of crisis:

Information management: governments need to collect, select, and process relevant information that sheds light on the causes and consequences of crisis-induced problems and the possible solutions that might be tried.

Critical decision-making: governments need to identify and prioritise solutions that will limit the impact of the crisis.
Collaborative management: governments must implement their solutions, and they cannot do this alone. They must work with other government organisations (including foreign and international organisations). They must align their knowledge, priorities, and resources with those of community and private actors to co-produce an integrated approach to arrive at coherent responses.

Crisis communication: governments will find it hard to produce an effective and legitimate response without the help of the population. They will need to inform, engage, and persuade citizens. In addition, it is important to inform all the members of the response network. The challenge is to offer a convincing narrative in the face of many competing counter-narratives. This project investigates the success and failure factors in the fulfilment of these four tasks in a detailed comparison of governmental responses to the same type of crisis (for instance, the Covid-19 pandemic in the Netherlands, Germany, Denmark, and Sweden).

Project 3: Ethics of crisis leadership
Crisis leaders must often choose between policy options that embody conflicting values. These moral dilemmas are particularly likely to occur in high-stakes situations with a necessity to act under conditions of deep uncertainty. In the context of the Covid-19 crisis, for instance, leaders had to weigh the importance of health protection against the freedom of movement.

This situation pitched the interests of the majority against those of a sizeable minority (think of curfews and vaccine passports).

This project will study how crisis leaders may develop and employ an ethical approach to decision-making in times of crisis. It will employ a case-study approach, investigating the decision-making processes in a small set of well-documented crises (for example, the Cuba Missile Crisis). It will study how crisis leaders handle ethical concerns raised by politicians and journalists. Moreover, it will study how perceptions of ethical decisions affect the perceived legitimacy of governmental crisis management performance.