Post-doc in Biomedical ImageAanalysis

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Post-doc in Biomedical ImageAanalysis

Riken Center for Brain Science
Country of Position: 
Contact Name: 
Henrik Skibbe
Subject Area: 
Machine Learning for Signal Processing
Bio Imaging and Signal Processing
Image, Video and Multidimensional Signal Processing
Start Date: 
03 March 2021
Expiration Date: 
30 June 2021
Position Description: 

Job offer: Post-doc in biomedical image analysis

We are seeking candidates who are interested in developing deep learning algorithms for improving the registration/alignment of our 2D/3D MRI and microscopy images. 

Job description

The selected candidate will join the interdisciplinary Brain/MINDS project which aims at studying the neural networks controlling higher brain functions in the marmoset, to gain new insights into information processing and diseases of the human brain.

As a member of the project, the selected candidate will contribute to the development and implementation of image processing and image analysis techniques with a focus on brain image data, specifically marmosets. We are seeking candidates who enjoy developing deep learning algorithms for 2D/3D image registration / alignment.

The work will be done in a highly interdisciplinary research group consisting of scientists from the neural-scientific and medical research fields. The emphasis is on developing cutting-edge technologies that improve current state-of-the-art and publishing high impact work in top-tier journals in order to build a substantial resume and strong international collaborations.

Experiences in biomedical image analysis is an advantage but not a requirement. This job may be a great opportunity to apply knowledge and expertise from the computer vision and/or image processing field to new problems in the biomedical field.


Wako-City (Kanto district, 2-1 Hirosawa, Wako, Saitama 351-0198). RIKEN is located in very close proximity to the northern part of Tokyo. Map:

The RIKEN campus is quite large and offers cafeterias, coffee shops, and a convenient store. From the nearest train station, it is only a 12 min train ride to the Ikebukuro-Station (Tokyo). The Ikebukuro-Station is a hub which connects many famous places in Tokyo, including Shinjuku (9 min train ride), Shibuya (18 min train ride) or Akihabara (19 min train ride). Many people prefer to avoid crowded streets and trains in their daily life and are living in close proximity to RIKEN. However, those who prefer living close to the sightseeing, nightlife and entertainment spots in Tokyo benefit from commuting out of the city in the morning, and returning in the evening (significantly less crowded than the other way around).


The candidate should have or be expecting to receive a Ph.D., by the time of employment, in related fields and have

  • relevant research skills and experiences in developing deep learning techniques for the analysis/processing of images, demonstrated by high-quality publications.
  • expertise in biological/medical/neural image processing, image registration, computer vision, machine learning, optimization, or similar fields, is an advantage.
  • good English communication skills
  • proficiency in a programming language (such as C++/Python/JS)
  • proficiency in tensorflow, pytorch or a similar DL library
  • good communication skills and ability to cooperate

Application & Employment

RIKEN employees enjoy the benefits of a generous vacation and leave package. An overview of benefits can be found here: working-at-riken. For application details, please refer to the official job posting URL:


IEEE SPS Educational Resources

IEEE SPS Resource Center

IEEE SPS YouTube Channel