MRI as a biomarker for Brain Amyloid deposition

📋 Type MA thesis
⚡ Status open
📅 Duration Jun 26, 2026 – Mar 1, 2027
👤 Primary supervisor Sheethal Bhat

Abstract

Accurate differentiation between Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) remains a significant clinical challenge, particularly during the early stages of disease progression. Amyloid PET imaging is an established method for assessing amyloid burden and supporting diagnosis; however, its availability is limited by cost, infrastructure requirements, and exposure to ionizing radiation.
Advances in quantitative MRI techniques provide an opportunity to identify imaging biomarkers that may reflect underlying pathological changes and potentially serve as non-invasive surrogates for amyloid deposition and disease characterization.

We are looking for strong applications to work in NIMHANS, Bengaluru, India on a 6-month masters thesis.

The project involves working closely with the Neurology and Neuropsychiatry department to develop AI/ML algorithms for Alzheimer research.

Essential Requirements:

  • Basic understanding of medical imaging, particularly MRI and neuroimaging data.
  • Strong foundation in image processing and computer vision concepts.
  • Proficiency in programming using Python.
  • Experience with scientific computing libraries such as NumPy, SciPy, Pandas, and Scikit-learn.
  • Strong background in Artificial Intelligence and Machine Learning, including deep learning frameworks such as PyTorch or TensorFlow.
  • Experience handling large datasets and developing end-to-end AI pipelines.
  • Familiarity with data preprocessing, feature extraction, model training, validation, and performance evaluation.

Preferred Skills:

  • Hands-on experience with neuroimaging or medical imaging datasets.
  • Familiarity with fMRI analysis tools and workflows (e.g., FSL, SPM, Nilearn, AFNI, FreeSurfer).
  • Understanding of machine learning applications in neuroscience and healthcare.
  • Experience with multimodal data analysis and time-series modeling is an advantage.
  • Knowledge of cloud computing, GPU-based training, and MLOps workflows is desirable.

Role Responsibilities:

  • Develop and evaluate AI/ML models using fMRI datasets.
  • Perform image preprocessing, quality control, and feature engineering.
  • Support research activities related to brain imaging and neuroscience applications.
  • Collaborate with MRI physicists, clinicians, and researchers to translate research questions into AI solutions.
  • Contribute to algorithm development, validation, and scientific documentation.

 

Please apply with your CV and transcripts uploaded to FAUBox and send the link to sheethal.bhat@fau.de

Candidates will need to interview with a panel to demonstrate medical engineering and Deep Learning proficiency.

The project will be completed in Bengaluru, India with supervision from FAU. Candidates that need an Indian Visa, may need to factor in additional visa processing time and time to apply for travel grants.