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Annonce

5 octobre 2020

Ultrasound Beamforming and 3D Image Reconstruction


Catégorie : Post-doctorant


Developping new methods for acquisition, reconstruction and analysis of ultrasound images with advanced computational, optimization and/or machine learning methods .

 

The purpose of the post-doctoral position is to conceive, implement and validate innovative methods to improve the quality of 2D and 3D ultrasound images resulting from the fusion of multiple signal samples, e.g. under different positioning and/or acquisition parameters. The successful candidate will take advantage of a recently arrived research ultrasound machine capable of parallel PWI acquisitions with 128 channels at a very high throughput rate while giving access to raw data. Methodologically, the challenges will be linked to the recovery of high-quality images (beamforming) improving detection and localization capabilities, modeling the uncertainty of the measures and favoring the image reconstruction speed. Given the possibility of controlling the acquisition through the computer the design of co-conception loops will also be considered to improve beamforming for specific applications.

 

The methods proposed during the research project should build on one or several of the following approaches:

üOptimization methods for solving inverse problems (sparsity constraints, global regularization, …)

üComputational tools for geometric multi-view image reconstruction

üStatistical and Machine Learning approaches (e.g. fusion, super-resolution, …)

 

Requirements

üPhD in (biomedical) engineering, computer science, signal processing, applied math or related fields.

üSolid background in mathematical optimization tools is required.

üExcellent programming skills.

üExperience in medical image analysis, preferably ultrasound.

üVery good English communication skills (written and spoken) .

üFrench is a plus.

More information following the link bellow:

https://box.ec-nantes.fr:443/index.php/s/qkPKtRpyazCAAgN

Send your CV and motivation letter to: diana.mateus@ls2n.fr

 

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