REMAP Fellowship GIST × KIST
REMAP Fellowship — REprogramming Metabolism And Plasticity
Open Positions

Five independent research projects — A through E

We are seeking postdoctoral researchers for five independent, interconnected research projects. Each fellow leads their own project with dedicated mentorship, resources, and intellectual ownership — spanning stimulation engineering, mechanism discovery, in vivo efficacy, and translational biomarkers.

A
Platform Engineering

UHF-MS Platform Engineering & Optimization

Design, build, and optimize a variable-frequency UHF-MS stimulation platform (10–400 kHz) for preclinical Alzheimer's disease research. Establish standard operating procedures (SOPs), validate output stability and thermal safety, and secure device-related intellectual property.

Key deliverables Platform SOP · device patents · platform validation paper
Mentors Prof. Jiyeon Kang & Prof. Hyuksang Kwon (GIST)
Desired background
  • PhD in electrical engineering, biomedical engineering, physics, or a related field
  • Experience with electromagnetic system design, coil engineering, RF/high-frequency circuits, or medical device development
  • Plus: familiarity with FEM simulation tools (e.g., COMSOL)
B
Neuroplasticity · Ex Vivo KIST link ~20%

Neuroplasticity Recovery Validation (Ex Vivo)

Investigate whether UHF-MS restores synaptic plasticity in Alzheimer's disease models using ex vivo hippocampal slice and cell-based assays. Build a stimulation parameter–response map linking specific UHF-MS conditions to plasticity outcomes (LTP, calcium dynamics, synaptic markers).

Key deliverables Parameter–response map · ex vivo efficacy data
Mentors Prof. Euiheon Chung (GIST) & Dr. Hoon Ryu (KIST)
Desired background
  • PhD in neuroscience, biomedical engineering, physiology, or a related field
  • Experience with electrophysiology (patch clamp, field recordings, MEA), calcium imaging, or hippocampal slice preparation
  • Plus: experience with AD mouse models (e.g., 5xFAD)
C
In Vivo Efficacy KIST link ~20%

In Vivo AD Therapeutic Efficacy

Validate the therapeutic effects of UHF-MS in Alzheimer's disease mouse models through behavioral, electrophysiological (EEG/EMG), and functional assessments. Identify responder vs. non-responder profiles and define the therapeutic parameter window.

Key deliverables Integrated efficacy paper · responder classification criteria
Mentors Prof. Tae Kim (GIST) & Prof. Euiheon Chung (GIST)
Desired background
  • PhD in neuroscience, pharmacology, behavioral neuroscience, or a related field
  • Experience with in vivo rodent behavioral testing (Y-maze, novel object recognition, fear conditioning, etc.)
  • Plus: experience with in vivo EEG/EMG recording and sleep analysis
D
Bioinformatics Single-cell · Spatial Transcriptomics KIST-resident · 100% on-site

Bioinformatics — Single-cell & Spatial Transcriptome Analysis

Decode how UHF-MS reshapes astrocyte reactivity, mitochondrial stress, and epigenetic regulation in AD using single-cell transcriptome and spatial transcriptome analysis. This KIST-resident bioinformatics fellow serves as the central data hub connecting GIST's functional readouts with molecular signatures.

Key deliverables Single-cell & spatial transcriptome atlas of UHF-MS response · mechanism paper
Mentors Dr. Hoon Ryu (KIST) & Prof. Euiheon Chung (GIST)
Desired background
  • PhD in bioinformatics, computational biology, molecular/cellular neuroscience, or a related field
  • Experience with single-cell transcriptome (scRNA-seq) and/or spatial transcriptome analysis pipelines
  • Plus: multi-omics integration, astrocyte/mitochondrial biology, or epigenetics
Note: This position is based full-time at KIST (Seoul), with regular collaboration visits to GIST (Gwangju).
E
Biomarker · AI KIST link ~10%

Translational Biomarker Discovery & AI-Driven Optimization

Develop a multimodal biomarker framework integrating EEG, fNIRS, behavioral, and molecular data to predict therapeutic response and optimize UHF-MS stimulation parameters using AI/machine learning approaches.

Key deliverables Biomarker classifier · stimulation optimization pipeline
Mentors Prof. Jaegwan Kim & Prof. Hyuksang Kwon (GIST)
Desired background
  • PhD in biomedical engineering, data science, computational neuroscience, or a related field
  • Experience with neuroimaging data analysis (EEG, fNIRS, fMRI), signal processing, or machine learning
  • Required: programming proficiency (Python, MATLAB, R)

Found your project?

Indicate your preferred project(s) in your cover letter. Applications are reviewed on a rolling basis until all positions are filled.

Apply Now