Forschungszentrum Julich, Germany invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at Forschungszentrum Julich, Germany.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Quantitative evaluation of (macro)economic impacts related to energy system transformations
- Possession of a master`s degree in economics or a related discipline from a reputable university
- Strong knowledge in econometrics / applied mathematics / applied quantitative methods, with the ability to apply this knowledge to real-world problems
- Proficiency in data analysis and programming using at least one statistical program such as R, Python, or similar programming languages
- Experience with GAMS, GTAP, and Exiobase is an asset.
- Skills in publishing research results in relevant scientific journals
- Excellent command of English, both spoken and written
- Understanding the German language is an asset
- Willingness and ability to collaborate effectively in an international, interdisciplinary team environment
- High level of flexibility, adaptability, and the ability to work under pressure while also demonstrating a strong commitment to achieving research goals
Deadline : Open until filled
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Representation and Active Learning for Multi-Scale Scientific Imaging
We are looking for a highly motivated candidate with a strong interest in foundational machine learning research and its application to real-world scientific problems. You should bring:
- A completed university degree (Master or equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field.
- Solid background in machine learning and/or computer vision.
- Interest in representation learning, active learning, uncertainty modeling, or decision-making under constraints.
- Experience with Python and modern ML frameworks such as PyTorch or TensorFlow.
- Curiosity for interdisciplinary research; prior experience with scientific or microscopy data is welcome but not required.
- Strong analytical skills, scientific creativity, and the ability to work independently while collaborating in a team environment.
Deadline : Open until filled
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD position – MISOWASP – Linking Molecular scale Interactions of Soil Organic matter with water retention in Soil-Plant systems
Soil organic matter (SOM) and extracellular polymeric substances (EPS) play a central role in soil structure, water retention, nutrient cycling, and plant-microbe interactions. Their molecular behavior has profound effects on soil hydraulic properties and biome stability. However, despite advances in continuum modeling of soil and plant-water systems, the molecular-scale mechanisms underlying these processes remain poorly represented. This PhD project aims to fill that gap by using molecular simulations to quantify the interactions of SOM and EPS with water and minerals, and to connect these insights with larger-scale soil and plant models.
- Perform molecular dynamics simulations to investigate interactions between soil organic matter (SOM), extracellular polymeric substances (EPS), water, and mineral surfaces
- Parameterize, set up, and validate molecular-scale models of complex soil-relevant systems
- Quantify structural, dynamical, and energetic properties governing water retention and soil hydraulic behavior
- Analyze molecular simulation trajectories to derive mechanisms relevant to nutrient cycling and plant–microbe interactions
- Translate molecular-scale findings into parameters and concepts usable in continuum-scale soil and plant-water models
- Contribute to the development and documentation of reusable, open, and reproducible molecular simulation workflows
- Collaborate with researchers working on soil physics, plant modeling, and multiscale simulation frameworks
- Present research results at scientific meetings and publish findings in peer-reviewed journals
Deadline : Open until filled
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Understanding catalyst/polymer interfaces for polymer recycling to hydrogen carrier molecules using X-ray methods
- Master`s degree in physics, chemistry, materials science, chemical engineering, or a related discipline
- Experience with surface X-ray scattering methods
- Experience using synchrotron radiation facilities
- Experience in catalysis and recycling
- Knowledge of interfaces science
- Knowledge in the field of energy storage
- Experience with programming languages (ideally Python)
- Fluent in written and spoken English
- Very independent and self-motivated way of working but also excellent teamwork skills
- High motivation to take on responsibility and to contribute to the development of research ideas
- Willingness for regular business trips
Deadline : Open until filled
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Student – Regeneration of coked catalysts form LOHC dehydrogenation
As part of your doctoral project, you will focus on the development of strategies for the regeneration of catalysts that deactivate in the release of hydrogen from the liquid organic hydrogen carrier (LOHC) perhydrobenzyltoluene. Your task will include:
- Planning and commissioning of a test rig for the dyhydrogenation of loaded LOHC
- Targeted deactivation of the catalyst materials under harsh process conditions
- Investigation of the underlying deactivation mechanisms
- Development of regeneration strategies for deactivated catalysts
- Coordination with internal and external project partners from industry and research
- Publication and presentation of research results in relevant journals and at national and international conferences
- Collaboration with other working groups at Forschungszentrum Jülich
- Participation in the development of the institute
Deadline : Open until filled
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Design and development of an in-house X-ray beamline for long term operando investigation of chemical hydrogen storage reactors
- Master`s degree in physics, chemistry, materials science, chemical engineering, or a related discipline
- Extensive knowledge of X-ray methods
- Knowledge of X-ray optics
- Knowledge of synchrotron science
- Knowledge of catalysis and energy storage
- Experience with programming languages (ideally Python), SPS controls system
- Fluent in written and spoken English
- Very independent and self-motivated way of working but also excellent teamwork skills
- High motivation to take on responsibility and develop own research ideas
- Willingness for regular business trips
Deadline : Open until filled
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Thesis in Physics, Materials Science or Chemistry – Tailored oxide membranes for energy applications
- Create a novel catalyst design, based on state-of-the-art thin film technologies and subsequent delamination of atomically defined oxides.
- Demonstrate the transfer process for heteroepitaxial systems.
- Develop materials design strategies to improve catalyst performances, such as activity and stability.
- Perform active research on the materials synthesis, characterization, and catalyst fabrication.
- Receive individual trainings in state-of-the-art methodology, comprising pulsed laser deposition (PLD), atomic force microscopy (AFM), advanced X-ray diffraction (XRD), and transport measurements (Hall effect and magneto-transport at low temperature), membrane-transfer techniques, Electrochemical benchmarking (RDE) and microscopy and spectroscopy techniques.
- Directly exchange with national and international project partners, embedded in an ERC-funded project.
Deadline : Open until filled
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Automation in Ink and Interface Characterization for Electrochemical Applications
You will be part of a research team that applies high-throughput experimentation to accelerate research in the emerging field of electrocatalysis. Catalyst inks are at the forefront of the development of new energy applications such as fuel cells and electrolysers. You will integrate and operate automated laboratory equipment to ensure reproducible and data-rich experiments. Close collaboration with chemists, engineers, and data scientists will enable the creation of a robust, high-throughput framework for future catalyst and layer development and efficient optimisation. The following topics will be part of this PhD position:
- (Semi-)Automated production of catalyst inks from commercial materials
- Development of characterization workflow for catalyst inks and interfaces
- Establishment of a research data management system and ontology
- Benchmarking state of the art materials as a baseline for the developed system
- Operation and further Development of an automated setup for catalyst inks and layers
- Method development of innovative characterization techniques for elecrocatalytic reactions
- Close collaboration with data scientists and automation specialists to develop a reliable, fast, material testing process
Deadline : Open until filled
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Inverse Design of Microstructures for Novel Sustainable Structural Metals
Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so-called lean alloys, and material systems with a high tolerance to impurities from the use of secondary raw materials are of particular relevance for improving recyclability and sustainability. Al-Ca is a promising lean alloy system for additive manufacturing, as the mechanical properties can be tailored through phase composition, distribution and morphology by tuning process parameters. The work is carried out within the DFG Priority Programme “DaMic – Data-driven Alloy and Microstructure Design of Sustainable Structural Metals” (SPP 2489), in close collaboration with a research partner responsible for the manufacturing and characterization of material samples.
As such, the position offers the opportunity to be involved in fruitful national collaborations. In this exciting job you can expect:
- to develop automated workflows for descriptor based microstructure reconstruction
- to identify material parameters for crystal plasticity simulations from experimental data through inverse analysis
- to establish structure–property linkages based on numerical simulations and to transform them into AI- and ML-ready information
- to develop and implement an indirect inverse optimization framework to identify microstructures that exhibit desired macroscopic properties
- to publish and present research results in relevant journals and at international conferences
Deadline : Open until filled
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Networks of complex neuron models: Learning in networks of spiking, highly non-linear neurons
You will participate in an international team in an EU-funded Doctoral Network project called MINDnet. The project consists of 15 PhD students at 7 universities, one research center and two companies. The project has partners from eight different EU countries. All 15 PhD projects are within the overall theme of neuromorphic computing and analog signal processing, targeting applications in the fields of communication, sensing, geo-localization, space and biomedical.
This PhD project will take place at PGI-14 Research Center Jülich. Apart from the time at PGI-14 there will be secondments of 3 months at Albira Tech SL (Barcelona, Spain), Technical University Graz (Austria), and University of Trento (Italy). There will also be regular meetings with the other 14 PhD students in the doctoral network, including 4 training schools and two workshops.
As a participant of the project, you will become part of the group ‘Computing in Dynamical Systems’ at PGI-14.
Deadline : Open until filled
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Hybrid electronic / photonic integrated neuromorphic computing systems for large-scale machine learning
You will participate in an international team in an EU-funded Doctoral Network project called MINDnet. The project consists of 15 PhD students at 7 universities, one research center and two companies. The project has partners from eight different EU countries. All 15 PhD projects are within the overall theme of neuromorphic computing and analog signal processing, targeting applications in the fields of communication, sensing, geolocalization, space and biomedical.
This PhD project will take place at PGI-14 Research Center Jülich. Apart from the time at PGI-14 there will be secondments of 3 months at HPE Belgium (Brussel), Spincloud Systems GmbH (Dresden), and Technical University Ilmenau. There will also be regular meetings with the other 14 PhD students in the doctoral network, including 4 training schools and two workshops.
As a participant of the project, you will become part of the group ‘Adaptive In Memory Computing Group’ at PGI-14.
Deadline : Open until filled
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Multi-Scale Data-Driven Model of Synaptic Function and Resilience
Healthy brain function relies on dynamic changes at the synapse. The relevant synaptic turnover and plasticity processes span spatial scales from the molecular up to the network level, and temporal scales from seconds to hours and beyond.
The aim of this PhD project is to build a multi-scale model linking molecular renewal to functional properties of synapses to study the relationship between synaptic resilience and the reliability of synaptic responses. The work primarily involves mathematical modeling and numerical simulation, but also the analysis of experimental datasets for model validation.
Deadline : Open until filled
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD position – Molecular simulation and machine-learning for predictive chromatography modeling with CADET – within the HDS-LEE graduate school
Chromatography modeling, while crucial for modern bipporcess development, still heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated into the open-source CADET simulation framework, enabling fully predictive process simulations without extensive experimental calibration. Embedded in the Helmholtz Graduate School for Data Science in Life, Earth and Energy (HDS-LEE), the project offers an interdisciplinary research environment at the interface of bioengineering, computational biophysics, and data-driven modeling, with strong links to open-source software development and industrially relevant applications. Tasks include:
- Development of molecular descriptors from protein structures and simulations
- Design and training of QSPR and machine learning models to predict ion-exchange isotherm parametersIntegration of predicted parameters into the CADET chromatography simulation framework
- Simulation and analysis of batch and gradient elution processes using predictive isotherms
- Curation and analysis of experimental chromatography data for model training and validation
- Collaboration with experimental and industrial partners
- Dissemination of results through high-quality publications and open-source software contributions
Deadline : Open until filled
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Additive Manufacturing and Resource Strategies for the Energy Transition
- Scientific analysis of additive manufacturing in the context of future production, material, and energy systems
- Research on the state of the art in additive manufacturing processes with a focus on costs, material usage, and energy requirements
- Investigation of materials used today and required in the future, as well as the underlying pre-chains and feedstocks
- Analysis of the extent to which conventional manufacturing methods and materials can be replaced
- Assessment of the impact of additive manufacturing on material flows, resource demand, and energy systems
- Classification of additive manufacturing in resource strategies and system models for evaluating long-term transformation paths
Deadline : Open until filled
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Ensuring Power System Security under Extreme Conditions using Exascale Computing
In this PhD project, you will shape the future of power system security by harnessing exascale computing. Your research will focus on understanding and enhancing grid resilience under extreme scenarios.
Your tasks:
- Conduct security and resilience studies for highly stressed systems (e.g., multiple faults, high renewable shares, or equipment failures).
- Evaluate system vulnerabilities and design recovery and stability strategies using large-scale simulation workflows.
- Build and expand realistic, continent-scale power system models (e.g., the European transmission grid).
- Implement and test GPU-capable, parallelized simulation frameworks.
- Work closely with experts in HPC and power systems to enhance scalability and computational performance.
- Disseminate your findings through scientific publications, conferences, and collaborations
- Supervise Bachelor’s and Master’s students and represent the institute in national and international research contexts
Deadline : Open until filled
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD-Position – Design and Operation of Local Energy Communities
The concept of Local Energy Communities (LECs) — where people, businesses, and local infrastructure share and manage energy collectively — is reshaping how energy is produced and consumed. Recognized by the EU as a cornerstone of the Clean Energy Transition, LECs empower local actors to participate actively in energy markets, support renewables, and enhance system flexibility.
In this PhD project, you will help design, simulate, and optimize these next-generation communities — making clean, local, and intelligent energy systems a practical reality.
Deadline : Open until filled
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Investigation of the influence of ionizing radiation on corrosion processes at the steel-glass interface
- Conducting in-situ and in-operando Raman experiments on glass corrosion
- Developing and constructing fluid cells with integrated alpha radiation sources
- Performing post-mortem analyses (electron microscopy e.g. EPMA, SEM, and X-ray methods e.g. µCT, XRD)
- Preparing and evaluating solution analyses using ICP MS/OES
- Publishing and presenting research results at national and international conferences
Deadline : Open until filled
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD position – Predicting the stress field in atherosclerotic plaque of arteries using scientific machine learning within the HDS-LEE graduate school
We are looking for a PhD student to develop learning-based surrogate models for predicting stress fields in patient-specific arteries. Especially high stresses in plaque can lead to rupture, which is one cause of a stroke and thus the prediction of plaque rupture is very relevant. The steps in the development of surrogate models are building data-driven models from medical imaging, extending them with physics-based approaches, and adapting existing physics-integrated neural network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning.
Deadline : Open until filled
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Process and plant engineering for chemical hydrogen storage
Possible doctoral research topics:
- Designing processes and plants for the synthesis and use of chemical hydrogen storage
- Integration of chemical hydrogen storage systems into industrial processes and infrastructures
- Development of control concepts for highly dynamic and load-flexible plants
- Coupling of technical, economic and ecological perspectives for optimised process design
- Experimental support for process development from laboratory to pilot to demonstration scale
- Working with a wide range of simulation tools such as CFD, numerial optimisation and artificial intelligence
Deadline : Open until filled
(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Interactive Learning for Closed-Loop Control of Microfluidic Live-Cell Experiments (HDS-LEE graduate school)
We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”.
Your Job:
- Develop physics-aware simulations of growing cell populations, including their spatiotemporal manipulation in microfluidic environments
- Design and implement reinforcement learning algorithms for control and manipulation, first in simulation and later on real experimental setups
- Refine a real-time planning and execution architecture for information-driven experiment steering (closed-loop control)
- Work in an interdisciplinary team of engineers, computer scientists, and life scientists
- Present your work at international conferences and learn about state-of-the-art methods in machine learning, reinforcement learning and computer vision for the life sciences
Deadline : Open until filled
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – High-Fidelity Multi-Instance Modeling of Living Symbioses: 3D+t Reconstruction of Paramecia and Symbionts from Live-Cell Microscopy (HDS-LEE graduate school)
We are looking for a PhD student in machine learning to work within a project linked to the “Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE)”.
Your Job:
- Develop 3D+t image reconstruction methods in a cell microscopy setting using image sequences as well as focus stacks
- Investigate instance and panoptic segmentation for endosymbionts and track them over time
- Implement, train and test novel machine-learning-based solutions on top-tier super-computing hardware
- Work in an interdisciplinary team of engineers, computer scientists, and life scientists
- Regularly participate in international conferences to present your own work, and learn about state-of-the-art machine learning and computer vision methods and their applications
Deadline : Open until filled
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD position – Co-regulation structures for large-scale single-cell transcriptomics – within the HDS-LEE graduate school
- Develop methods and workflows to construct robust co-regulation networks from large single-cell and spatial transcriptomics datasets
- Integrate ontologies and metadata (e.g., tissue, cell type, developmental stage, treatment) to build tissue- and context-specific co-regulation networks
- Design and implement clustering and integration approaches (e.g., network-based and subspace clustering)
- Use co-regulation networks for gene function and protein–protein functional relationship prediction (guilt-by-association), and benchmark them against existing bulk co-expression resources
- Compare and optimise the developed methods on real biological datasets; work closely with experimental partners for interpretation and validation of results
- Contribute to the integration of the developed methods into open-source software tools and data portals of the institute • Collaborate with internal and external, as well as national and international, project partners
- Present your results at national and international conferences
- Prepare scientific publications and project reports
Deadline : Open until filled
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD position – Probabilistic Metabolic Flux Analysis within the HDS-LEE graduate school
This PhD project develops a Bayesian inference framework for hybrid model- and data-driven modeling of metabolism, with a particular focus on handling model misspecification. By combining Bayesian computational statistics, differentiable programming, and high-performance computing, the project aims to deliver robust, interpretable, and scalable methods for metabolic flux analysis.
You will:
- Design hierarchical models that explicitly capture misspecifications in metabolic models
- Develop differentiable and scalable inference algorithms using automatic differentiation
- Implement HPC-tailored sampling strategies in Python and C++
- Apply your framework to analyse real biological datasets to demonstrate robustness, interpretability, and practical impact
- Contribute to open-source software tools, helping to shape future research infrastructure
- Present your results on conferences in Germany and abroad
Deadline : Open until filled
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD position – Understanding the Transport of Aerosol Particles in the Upper Troposphere and Lower Stratosphere and Their Interaction with Ice Clouds
Aerosol particles act as ice nucleating particles, providing surface for water vapour to deposit and freeze into ice particles. Formation of cirrus clouds and subsequent sedimentation of ice crystals can dehydrate air in the upper troposphere and lower stratosphere (UTLS) region, thereby influencing the water vapor budget of this region. Aerosols and cirrus are important to the global climate because they interact with radiation from the sun and from the earth. However, the aerosol-cirrus interaction in the UTLS is not well understood because of lacking sufficient in situ data. In addition, the dynamics and transport processes across the UTLS adds complexity to unveil the role of UTLS aerosols in cirrus formation and the life cycle of cirrus. Therefore, it is crucial to measure aerosol composition and properties together with cirrus occurrences in the UTLS for achieving a better understanding of the link between aerosols and cirrus clouds in the UTLS, for which Lagrangian modelling is a valuable tool to revealing the source of UTLS aerosols, the origin of water masses, and formation processes of cirrus particles.
Your key responsibilities include:
- Preparation, operation, and calibration of aerosol, water vapour and cloud instruments through the research aircraft campaign
- Flight planning for aerosol and cirrus measurement missions using Lagrangian modelling
- Evaluation and interpretation of in-situ measurement data aided by
Deadline : Open until filled
About Forschungszentrum Julich, Germany –Official Website
Forschungszentrum Jülich is a member of the Helmholtz Association of German Research Centres and is one of the largest interdisciplinary research centres in Europe. It was founded on 11 December 1956 by the state of North Rhine-Westphalia as a registered association, before it became “Kernforschungsanlage Jülich GmbH” or Nuclear Research Centre Jülich in 1967. In 1990, the name of the association was changed to “Forschungszentrum Jülich GmbH”. It has close collaborations with RWTH Aachen in the form of Jülich-Aachen Research Alliance (JARA).
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