【13-A. Dr. N.Jon Shah】

◼ Research Field
  - Topic: Advanced MRI research: from system hardware to quantitative brain imaging

Magnetic Resonance Imaging (MRI) is one of the most powerful neuroscientific and diagnostic tools to access the in vivo human brain non-invasively. At ultra-high magnetic fields (such as 7 Tesla), MRI further offers exceptional levels of detail – but also brings exciting scientific and multi-disciplinary challenges including hardware design, data acquisition and image reconstruction.

Under my leadership, the institute of neuroscience and medicine – 4 (INM-4), Forschungszentrum Jülich primarily focuses on the development, experimental validation and the clinical and preclinical implementation of novel and multimodal neuroscience methods. We have established a globally unique platform for translational neurological research based on combined ultra-high field MRI and PET. Furthermore, the institute’s activities are embedded in national and international networks and collaborations. Therefore, the selected students can experience how cutting-edge MRI research works across multiple levels, ranging from building the technology itself to developing advanced data-analysis pipeline. They may focus on one or more research areas that match their background and interests while being exposed to the broader landscape of MRI science. A few of potential topics are below.

  • Next-generation MRI hardware development: Students can learn how MRI hardware is designed, simulated and tested. This includes working with radio-frequency coil designs, understanding how hardware influences image quality and gaining hands-on experience with coil testing or measurement steps. This offers a rare opportunity to see the engineering side of MRI and how hardware innovations can be combined with cutting-edge software innovation leading to new imaging capabilities. This training offers a rare opportunity for the students.

  • Advanced MR data acquisition, reconstruction, and analysis: Students will also be introduced to how MRI signals are acquired and converted into images. They can learn about signal processing, image reconstruction and quantitative brain mapping, and both classical and data-driven analysis methods using AI and deep learning. AI-aided reconstruction is rapidly gaining acceptance in community and is thus right at the forefront of MRI research; students trained in AI reconstruction are highly sought after by academia and industry.

  • Structural, metabolic, molecular and functional brain imaging, and spectroscopy: my institute also conducts research on how MRI and MR spectroscopy can be used to study brain structure, physiology, metabolism and function. Students will be exposed to a variety of imaging contrasts and scientific questions, and will have opportunities to attend seminars and group meetings covering diverse MRI applications.

Overall, students participating in this internship will be able to closely engage with our activities under supervision by myself and our expert in the topic.

◼ Required Research Field of Study
  - MRI is a multi-disciplinary field, and we look for highly motivated students from medicine, physics, chemistry, biology, engineering or computer science.

◼ Description of Research Activities During the Program
  - During the 6-month internship, the student will be involved in hands-on MRI research that can span hardware and data acquisition, reconstruction, or quantitative imaging, depending on the student’s background and interests. Exposure to AI / machine learning will comprise a significant part of these projects. The projects will be designed to provide both foundational training and a focused research component, with flexibility to adapt as the student progresses.

  - Month 1 (Sep. 2026) - Orientation, Setup, and Foundations
The student will begin by settling into the research environment, setting up the required software tools and learning core concepts in MRI.
This includes introductory reading, basic tutorials, and first simple exercises such as loading datasets, visualising signals and performing basic Fourier-based image reconstruction.

     - Months 2-3 (Oct.-Nov.) – Exposure to Different Research Components
  In the following months, the student will be introduced to a range of possible research directions pursued in the group:
  1. Hardware & RF coil development
  2. Image reconstruction and quantitative mapping
  3. Data processing, signal analysis and interpretation 

  - Months 4-5 (Dec.-Jan.) – Focused Project Work
After gaining an overview of the research topics, the student will focus on one selected project direction.

  - Months 6 (Feb.) – Final Analysis, Documentation & Presentation
In the final month, the student will summarise findings in a short technical report, document code and analysis steps, and prepare and deliver a final presentation to the team.

   - Mentoring environments

The student will be integrated into an active and supportive research environment. In addition to regular one-to-one guidance from supervisors, the student will benefit from multiple learning opportunities within the institute:
  • Weekly group meetings, where ongoing research projects are presented and discussed.
  • Monthly team meetings, which bring together researchers working on specific MRI methods. This provides an opportunity to learn from ongoing projects, ask questions and receive feedback from senior scientists.
  • Monthly student meeting, where students from different groups (e.g. PET, Digital Translational Neuroimaging) in INM-4 present and exchange ideas. This broader interaction allows the student to gain exposure to different imaging modalities and improve communication skills.
  • Monthly Korean Researchers’ Seminar Series where Korean scientists working at FZJ give seminars about their research and build a sense of community. This fosters a supportive cultural community and gives the student additional opportunities to learn other things beyond their project. We also have several colleagues from South Korea who will also participate in events with the students.
  • Open door environment – students are encouraged to interact with postdocs and PhD researchers, seek feedback and ask for help when needed.

◼ Research Equipment or Software to be Used
  - Equipment & Data Access:
  Two 3 T clinical scanners (one with BrainPET insert)
  One 7 T clinical scanner (with a unique BrainPET insert)
  One 7 T animal scanner

  - Computing Resources
  No GPU or HPC infrastructure may not be required for the student’s project, but optional access to lab computing clusters and to the Europe’s fastest supercomputer (JUPITER) can be arranged if the student explores more advanced reconstruction or AI-based extensions. 

  - Software & Programming Tools
  Python or MATLAB
  Other MRI related toolboxes and software

◼ Website
  - https://www.fz-juelich.de/de/inm/inm-4

【13-B. Dr. Seong Dae Yun】

◼ Research Field
  - Brief introduction:

This project aims to apply state-of-the-art deep learning techniques to ultra-high resolution functional magnetic resonance imaging (fMRI), under the scientific leadership of Dr. Seong Dae Yun, Team Leader of the Sequences and Scientific Computing Team at INM-4, Forschungszentrum Juelich (FZJ). MRI provides a unique, non-invasive window into the living human brain and represents one of the most powerful modalities for probing neural function in vivo.

The integration of deep learning-based image reconstruction with ultra-high-resolution fMRI is still in its early stages, yet it holds substantial innovative potential for the neuroscience community. By overcoming fundamental limitations of conventional fMRI techniques, the proposed approach enables the investigation of brain function at the mesoscale level, offering deeper insights into the brain’s intrinsic computational architecture.

Beyond basic neuroscience, the developed methods are highly relevant for clinical research, enabling improved characterization of functional alterations in neurological conditions such as brain tumors and affective disorders. Overall, the project provides an interdisciplinary research environment at the interface of neuroscience, medical imaging, and artificial intelligence, offering excellent training opportunities for students interested in cutting-edge brain imaging and computational methods.

Students joining this project will gain hands-on experience in MRI physics, advanced data acquisition, and modern AI-based reconstruction techniques applied to real neuroimaging data, with opportunities to contribute to research abstracts and publications in leading international conferences and SCI journals.

Keywords: Neuroscience, Brain Function, MRI, fMRI, Ultra-High Resolution Imaging, Data Acquisition and Reconstruction, Deep Learning, Artificial Intelligence, Image Analysis, Clinical Neuroimaging, Neurological Disorders, Neurodegenerative Diseases

◼ Required Research Field of Study
  - Applicants with background or interest in the following fields are encouraged to apply. Prior expertise in MRI or related technical domains is not required. Familiarity with a subset of the areas below is desirable but not mandatory, and motivated students from diverse academic backgrounds are welcome. The Principal Investigator, Dr. Seong Dae Yun, will provide comprehensive scientific guidance and hands-on training throughout the research period.

◼ Description of Research Activities During the Program
   1. Research Purpose:
The human neocortex, a ~3–5 mm thick sheet forming the outer layers of the brain, underlies our highest cognitive and perceptual functions. Its structure is organized into distinct cortical layers, each characterized by specific neuronal populations, connectivity patterns, and computational roles. Non-invasive investigation of human brain function at such layer-specific spatial scales can be achieved using functional magnetic resonance imaging (fMRI). As the laminar-level investigation provides critical insights into the fundamental computational units of the brain, an increasing number of fMRI studies have focused on this direction.

Despite substantial progress, reliable detection of layer-specific neural activity remains challenging. While submillimeter-resolution fMRI techniques have been developed to probe cortical layers, the inherently low signal-to-noise ratio (SNR) at this spatial scale often hampers robust and reproducible characterization. In addition, standard echo-planar imaging (EPI) techniques commonly used in fMRI impose limitations on the accurate spatial correspondence between functional signals and anatomical references.

This project aims to address these challenges by applying state-of-the-art deep learning techniques (Yun et al., 2025) to a novel ultra-high-resolution fMRI framework based on TR-external EPIK (Yun et al., 2022), under the scientific leadership of Dr. Seong Dae Yun. Specifically, the deep learning framework is designed to reconstruct fMRI images with enhanced signal-to-noise ratio (SNR) and improved structural fidelity, while further increasing effective spatial resolution through super-resolution strategies.

As the application of deep learning to layer-specific fMRI remains at a frontier stage, this project represents a cutting-edge methodological advancement beyond current state-of-the-art techniques. By enabling more reliable and accurate laminar-level functional mapping, the proposed framework establishes a strong foundation for translational applications, including clinically relevant studies in patient populations such as individuals with brain tumors, where precise functional characterization is of critical importance. 

  2. Research Tasks
    2.1. Initial Training & Research Environment Setup (Month 1)
      - Dr. Seong Dae Yun will provide introductory lectures covering the fundamental principles of MRI and functional MRI (fMRI).
      - The student will set up the research environment required for the project, including obtaining MRI system access (Level 1), configuring access to high-performance supercomputing resources, and installing software packages for data processing and analysis (e.g., SPM, FSL, and ANTs).
      - In parallel, the student will conduct guided literature reading to build a solid foundational understanding of MRI and fMRI methodologies relevant to ultra-high-resolution imaging.

                 2.2. Ultra-High Resolution fMRI Sequence (Months 2 - 3)
      - An ultra-high-resolution fMRI sequence based on TR-external EPIK will be implemented on a 7T MRI scanner, targeting whole-brain coverage with 0.4–0.5 mm spatial resolution to enable the investigation of layer-specific neural activity. Multiple imaging protocols will be systematically evaluated to determine optimal acquisition parameters.

     - The student will complete the required MR scanner operation courses for scanning healthy human participants (Level 2 / Level 3), becoming qualified to operate the MR scanner independently under institutional guidelines.
    - Based on intermediate methodological results, research abstracts will be prepared and submitted to relevant international conferences.

   2.3. AI-driven Deep Learning fMRI Reconstruction (Months 4 - 6)
    - A deep learning-based reconstruction framework will be developed to address the intrinsically low signal-to-noise ratio (SNR) and image artefacts associated with layer-specific fMRI.
    - In addition, a super-resolution reconstruction strategy will be implemented to further enhance effective spatial resolution by approximately 15–20%, thereby improving the accuracy of laminar-level response characterization.
    - These methods will be developed and trained using supercomputing resources.
    - The performance of the proposed approaches will be quantitatively evaluated against conventional reconstruction techniques using established image quality and functional metrics.

   2.4. Data Acquisition, Analysis and Scientific Writing (Months 5 - 6)
    - In parallel with sequence development and reconstruction work, fMRI data will be acquired from healthy human participants using a 7T MRI scanner.
    - The student will learn advanced fMRI data processing and analysis workflows to accurately map functional activity onto high-resolution anatomical images.
    - The developed methods will subsequently be applied to fMRI datasets from patients with brain tumors, enabling more precise characterization of functional alterations in tumor-affected brain regions.

 3. Expected Outcomes
  3.1. “Hands-on experience with cutting-edge fMRI technology”:
  The student will acquire hands-on experience in operating 7T MRI systems, performing ultra-high-resolution fMRI experiments, and gaining an in-depth understanding of the mechanisms underlying human brain function.

  3.2. “Expertise in AI-based image reconstruction and high-performance computing”:
  Through the development and evaluation of deep learning-based reconstruction models on high-performance supercomputing platforms, the student will acquire strong computational and analytical skills directly applicable to careers in artificial intelligence, imaging science, and biomedical engineering.

  3.3. “Opportunities to contribute to international conferences and publications”:
  The student will actively participate in the preparation of conference abstracts and manuscripts for submission to leading international conferences and high-impact peer-reviewed journals, thereby building a competitive early-stage research portfolio.

  3.4. “Strong foundation for future academic researcher”:
  The student will develop technical proficiency, research independence, and collaborative skills, providing solid preparation and motivation for future academic training at the Master’s and PhD levels.

◼ Research Equipment or Software to be Used
  - This project will make use of the following research equipment and software environments.

   1) Research Equipment: 3T/7T MRI Scanners with fMRI Stimulation Systems
    Our group operates a state-of-the-art 7T Siemens Magnetom Terra scanner as well as a widely used clinical-standard 3T Siemens Prisma system. These MRI platforms are optimized for advanced structural, functional, and physiological imaging of the human brain. Both systems will be utilized in this project to acquire high-quality, ultra-high-resolution neuroimaging data for methodological development and validation.

   2) High-Performance Computing (HPC) Resources
    Dr. Seong Dae Yun has dedicated access to high-performance supercomputing resources at FZJ for advanced MR image reconstruction and deep learning research. These resources enable the training of high-capacity neural networks on large-scale neuroimaging datasets—computational tasks that are not feasible on standard workstations—and substantially accelerate algorithm development, testing, and optimization.

  3) Image Reconstruction & Scientific Computing: MATLAB, Python, C/C++
    This project will employ MATLAB, Python (including deep learning frameworks such as PyTorch and TensorFlow), and C/C++ for algorithm development, numerical simulation, and data analysis. Prior experience with these environments is advantageous but not mandatory. Familiarity with Linux-based systems is recommended to ensure an efficient workflow and effective use of HPC resources.

      4) Functional MRI Data Processing Tools: SPM, FSL, ANTs
  Functional MRI data acquired in this project will be processed using widely adopted neuroimaging toolkits such as SPM, FSL, and ANTs. These platforms provide essential tools for fMRI preprocessing, statistical modeling, and high-accuracy anatomical registration, forming the backbone of reliable and reproducible functional neuroimaging analysis.

   ※ any specific requirements or important information

      - Students will actively contribute to ongoing scientific research while gaining practical skills in functional MRI and deep learning techniques. As the project is research-driven, students with genuine interest, curiosity, and a strong motivation to learn will benefit most from the experience. Based on achievements, students will have opportunities to write research abstracts and papers for submission to leading international MRI conferences and high-impact journals.

◼ Website
  - https://www.fz-juelich.de/en/inm/inm-4

【13-C. Dr. Kyesam Jung】

◼ Research Field
  - This program is in the field of computational neuroscience including cognitive neuroscience, psychology, clinical neuroscience, network neuroscience and multimodal neuroimaging. We focus on relationships between brain and behavior using cognitive and clinical neuroimaging data.

◼ Required Research Field of Study
  - Neuroscience, Psychology, Computational Neuroscience, Network Neuroscience, Cognitive Neuroscience

◼ Description of Research Activities During the Program
  - Overview
  This 6-month internship offers undergraduate students an opportunity to participate in clinical or cognitive neuroscience projects that combine experimental task design, multimodal neuroimaging, network analysis, and machine learning. You will work with multimodal MRI data to explore relationships between human brain and behavior.

   You will gain hands-on experience with:

    - Cognitive task paradigms related to attention and cognitive control
    - Processing multimodal MRI data (e.g., structural and functional neuroimaging)
    - Brain connectome construction and analysis
    - Basic meta-analytic and statistical methods used in human brain MRI
    - Applying machine learning to neuroimaging data

     Training Environment and Supervision
The goal of the internship is for you to experience the full research cycle—from formulating questions to interpreting data and writing up results—and to contribute to the main parts of a research paper. The project will be conducted under the supervision of the principal investigator (Kyesam Jung) and the group leaders in INM-7 who will provide regular feedback and mentoring.

◼ Research Equipment or Software to be Used
  - FreeSurfer, FSL, SPM, Python, MATLAB

    ※ any specific requirements or important information
      - The project is suitable for highly motivated students with an interest in cognitive neuroscience, clinical neuroscience, psychology, or computational neuroscience. Prior experience with MRI or advanced programming is helpful but not mandatory; guidance and training will be provided through the program.

◼ Website
  - https://www.fz-juelich.de/en/inm/inm-7

◼ Research Field
  - The Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons in Jülich hosts one of the strongest research groups internationally in aberration-corrected high-resolution transmission electron microscopy method development and applications to problems in solid state research. The Ernst Ruska-Centre operates more than 15 electron microscopes, including a next generation FEI PICO chromatic aberration corrected transmission electron microscope with a resolution of 50 picometers and a double biprism dedicated transmission electron microscope for magnetic imaging using off- axis electron holography.

◼ Required Research Field of Study
  - Materials science, physics or a related field.

◼ Description of Research Activities During the Program
  - Electron microscopy of materials.

◼ Research Equipment or Software to be Used
  - Advanced transmission electron microscopes.

◼ Website
  - https://er-c.org/

◼ Research Field
  - At the INW Institute Division INW-1 “Catalytic Interfaces”, the focus is on the elementary processes on the catalyst surface during the de(hydrogenation) of hydrogen storage molecules. Our focus is on maximizing productivity, efficiency, and selectivity in order to optimize performance, mitigate losses, and degradation and reduce costs. The aim is to understand reaction and degradation mechanisms at the molecular level at the catalytically active interfaces in order to further extend the life cycle of the storage molecules through optimized catalyst materials. Another aim is to avoid the use of precious metal components, e.g. by developing new types of alloy catalysts. We are also researching new storage molecules, e.g. with reduced dehydrogenation energy or of biogenic origin, for which the elementary processes on the catalyst surface, suitable material combinations and relevant degradation mechanisms must be clarified and fundamentally understood. In addition to the focus topic of chemical hydrogen research and the corresponding focus molecules (e.g. methanol, DME, ammonia, methane, formic acid, LOHC), we also conduct research in the fields of electrochemical energy storage, water desalination, and direct lithium extraction.

In this context, we at INW-1 conduct basic and application-oriented research on mechanisms and processes at the atomic to mesoscale level with a focus on interfacial and transport phenomena. We work closely with the other Institutes of INW and Forschungszentrum Jülich to accelerate technology development in a holistic way. We interpret the gained fundamental understanding in the context of the material and device performance and thus contribute to predictable and scalable knowledge for rational design and new concepts for improved materials and processes. To investigate the corresponding phenomena, we use and develop X-ray and neutron-based methods across time and length scales and apply corresponding advanced (big) data analytics tools and machine learning. In this context, we operate corresponding advanced infrastructure and measurement equipment.

◼ Required Research Field of Study
  - Physical chemistry, Analytic chemistry, Catalysis, Electrochemistry, or Spectroscopy

◼ Description of Research Activities During the Program
  - Joining fundamental researches on hydrogen storage (electro)catalysis with real-time/operando advanced spectroscopy/microscopy

◼ Research Equipment or Software to be Used
  - Potentiostat, FT-IR spectroscopy, Raman spectroscopy, Mass spectrometry

    ※ any specific requirements or important information
      - INW-1 at Forschungszentrum Julich is actively developing advanced analytic methodologies with real-time monitoring of catalytic systems under realistic conditions. Anyone interested in understanding real structural dynamics of catalytic systems is welcome!

◼ Website
  - https://www.fz-juelich.de/de/inw/unsere-bereiche/inw-1

https://www.fz-juelich.de/en/inw/overview-inw-1/catalytic-interfaces-for-chemical-hydrogen-storage-inw-1/fundamentals-of-chemical-hydrogen-storage/catalytic-interfaces 

【29-A. Dr. Vanessa Maybeck】

◼ Research Field
  - For neuro-electronic hybrid systems such as neural implants or prostheses, the interface between the neuronal network and engineered surfaces is of critical importance. An ideal neural interface should form a stable, adhesive link between neurons and an external device, while still allowing active communication with the cells. To date, micro- and nanoscale test systems have employed advanced materials such as carbon nanotubes, silicon nanowires, conducting polymers, graphene, or organic–inorganic hybrids to promote neuronal growth and enable signal recording or stimulation. In parallel, upconversion nanoparticles (UCNPs) have emerged as promising tools for activating light-sensitive proteins in optogenetics.

Optogenetics makes use of light-sensitive proteins—originally found in algae or microbes—that can be genetically expressed in mammalian neurons to control ion currents using light. Depending on the protein type, neurons can be either depolarized or hyperpolarized. However, the short-wavelength light (blue or green) that these proteins typically respond to suffers from poor tissue penetration and can cause phototoxic effects during prolonged illumination. This challenge motivates the search for strategies that deliver precise, localized light stimulation while minimizing tissue damage.

UCNPs provide an elegant solution. These nanomaterials absorb near-infrared (NIR) light—which penetrates tissue deeply and is far less phototoxic—and converts it into higher-energy visible light locally at the target cell. They combine several advantageous properties: low toxicity, high photostability, large Stokes shifts (the energy difference between absorbed and emitted light), narrow emission spectra, and minimal tissue autofluorescence.

Recent research continues to expand UCNP performance and versatility. New synthesis methods have improved crystallinity and upconversion efficiency (Farva et al., 2025). Polymer-coated UCNPs have enhanced biocompatibility and functional tunability for biomedical applications (de Freitas Silva et al., 2025). UCNPs are now even being explored for powering molecular motors using NIR light—highlighting the broad potential of these nanomaterials (Sheng et al., 2025).

Despite this progress, UCNPs have not yet been widely used to functionalize neural implants for optogenetic stimulation. Current approaches often rely on implanting separate optical components or multiple devices, which can increase surgical trauma and reduce the implant’s electrical performance. Our research explores a more integrated strategy: using UCNPs both as light-emitting stimulation sites and as adhesive bridges between neurons and implant surfaces. This dual role could preserve electrical recording capability while adding optical control. Importantly, UCNPs generate far fewer photons than direct laser illumination, helping to reduce phototoxicity in living cells.

Integrating UCNPs into neural implants presents several engineering challenges. Achieving efficient optogenetic stimulation requires close proximity between the UCNPs and light-sensitive proteins, demanding precise control of the device–UCNP and UCNP–cell interfaces. Optimization of UCNP surface chemistry is therefore crucial to promote strong cell adhesion and stability. In this research stay, UCNPs will be used to combine optical and electrical functionality in next-generation neural interfaces. With ongoing advances in synthesis, coating, and biointegration, these materials open exciting opportunities for safer, smarter neurotechnologies.

◼ Required Research Field of Study
  - Chemistry or Chemical Engineering or Electrophysiology

◼ Description of Research Activities During the Program
  - Assist in synthesis of upconversion nanoparticles and their characterization with optogenetic devices.

◼ Research Equipment or Software to be Used
  - The project will use amplifiers and software developed by the institute as well as sterile technique for primary cell culture, microfabrication tools, and chemical synthesis equipment including a Schlenk-line.

◼ Website
  - https://www.fz-juelich.de/de/ibi/ibi-3

【29-B. Dr. Hans-Joachim Krause】

◼ Research Field
  - The Magnetic Field Sensors group is working on frequency mixing magnetic detection of nanoparticles for biosensing application, for instance magnetic immunoassays, and on low field nuclear magnetic resonance. The aim is to establish magnetic label-based immunoassays which employ the highly specific interaction between antigenes and antibodies in conjunction with magnetic nanoparticle markers for the detection and quantification of specific biomolecules. Research Topics include measuring the magnetic response of magnetic nanoparticles by Frequency Mixing Magnetic Detection (FMMD), and magnetic particle actuation by applying a magnetic gradient field with magnetic tweezers.

◼ Required Research Field of Study
  - Physics or Electrical Engineering or Informatics or similar

◼ Description of Research Activities During the Program
  - Research on Frequency Mixing Magnetic Detection and development of dedicated measurement instrumentation

◼ Research Equipment or Software to be Used
  - Custom-made measurement instrumentation, Python software

◼ Website
  - https://www.fz-juelich.de/en/ibi/ibi-3/organization/magnetic-field-sensors

◼ Research Field
  - The Spatial Economics team explores how energy transition processes affect economies on various regional levels, analyzing their implications for stakeholders and offering insights to inform science and society. With foundations in economics, econometrics, and policy assessment, we develop tools for providing levers that facilitate smooth energy system transition pathways aligned with climate goals and stakeholder demands. Applying cross-sectional and panel data for, e.g., Germany, the EU, and international contexts, our analyses give insights into e.g. regional economic expansion potential of renewables, multi-regional decision analyses, and provide distributive assessments for energy technologies from an economic perspective. Application focuses lie in wind power and general effects of renewables after end-of-life (circular economy), the economic effects of carbon dioxide removal and sustainable transition of energy intensive industries. Our audience includes scientists, policymakers, and decision-makers seeking informed strategies for navigating energy transitions and transformation processes.

◼ Required Research Field of Study
  - Economics, Econometrics, Applied Mathematics, Environmental Science, Energy Science, Social Sciences, Sustainability, or related programs

◼ Description of Research Activities During the Program
  - Collaboratively formulate a research question aimed at conducting a self-directed project within the team’s research scope. Contribute to analyses, research, and writing to produce and submit a research paper. Participate in literature reviews, attend and engage in regular team and institute meetings, and actively contribute to project discussions

◼ Research Equipment or Software to be Used
  - Laptop computer including necessary software will be provided. Possibility to use the institute's cluster server and energy system model.

    ※ any specific requirements or important information
      - Willingness to work in a highly interdisciplinary research environment

◼ Website
  - https://www.fz-juelich.de/en/ice/ice-2