View source for Postdoctoral Position in Biophysical Modeling for Advanced qMRI Applications

{{Job Announcement
  | image=donders_nwo.png
  | title=Postdoctoral Position in Biophysical Modeling for Advanced qMRI Applications
  | short_text=This position is part of a project focused on developing efficient and robust models to extract quantitative biomarkers from MRI data, aiming to accelerate and enhance clinical applications in neurodegenerative, neuro-inflammatory, and neurovascular diseases.
The selected candidate will lead the development and integration of neural network decoding strategies for Multi-Compartment Relaxometry (MCR) and Myelin Water Imaging (MWI) as a new tool for assessing brain tissue microstructure. The work will focus on enhancing computational efficiency, improving biomarker extraction, and expanding the biophysical models to include vascular tissue factors.
  | type=Postdoc
  | location=Radboud University, Nijmegen, The Netherlands.
  | deadline_year=2024
  | deadline_month=12
  | deadline_day=31
  | salary_start=
  | salary_end=
  | salary_currency=
}}

We are seeking a highly motivated Postdoctoral Researcher to join our interdisciplinary team working on cutting-edge biophysical modelling for myelin water imaging (MWI) and quantitative MRI techniques. This position is part of a project focused on developing efficient and robust models to extract quantitative biomarkers from MRI data, aiming to accelerate and enhance clinical applications in neurodegenerative, neuro-inflammatory, and neurovascular diseases.
The selected candidate will lead the development and integration of neural network decoding strategies for Multi-Compartment Relaxometry (MCR) and Myelin Water Imaging (MWI) as a new tool for assessing brain tissue microstructure. The work will focus on enhancing computational efficiency, improving biomarker extraction, and expanding the biophysical models to include vascular tissue factors.

== Key Responsibilities: ==
* Develop and optimize neural network-based methods for MCR-MWI and Diffusion Informed MWI.
* Collaborate with other team members to test and apply these models to data from different MRI systems and field strengths (1.5T, 3T, 7T).
* Lead efforts to develop biomarkers beyond Myelin Water Fraction (MWF), incorporating new data related to tissue vascularization and metabolic oxygen rates (CMRO2).

== Requirements: ==
* PhD in physics, biomedical engineering, medical imaging, computer science, or a related field.
* Expertise in MRI data analysis, biophysical modeling, and/or neural networks.
* Strong programming skills (Python, MATLAB, or similar) and experience with machine learning techniques.
* Familiarity with MRI pulse sequences and quantitative MRI methods (such as DWI, MWI or QSM) is highly desirable.

== What We Offer: ==
* An exciting research environment within Radboud University, Nijmegen, one of the leading institutions in neuroimaging, where a 14T human scanner will soon be installed.
* Access to state-of-the-art MRI facilities and computational resources.
* Opportunities for professional development in postdoctoral program and collaboration with international partners (Kwok-shing Chan - MGH).

== Location: ==
Radboud University, Nijmegen, The Netherlands.

== Application Process: ==
Applications will open formally in mid-october. A CV, cover letter and contact information for two references.

For more information contact '''José P. Marques''': [mailto:jose.marques@donders.ru.nl jose.marques@donders.ru.nl]