Image, Video and Multidimensional Signal Processing

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IVMSP

Signal Processing - R&D Engineer

Seenel Imaging is hiring its R&D Engineer or equivalent specialized in Signal Processing and C++ Software

www.seenel-imaging.com

Spin off of an INSERM lab located in a University hospital, Seenel Imaging is a start up dedicated to functional Neuro-imaging for Research application. Founded in 2018, Seenel Imaging launched Medelopt®, a wearable research device combining EEG/fNIRS. 

Post-Doc Position in AI-based Face Recognition Explainability

Face recognition has become a key technology in our society, frequently used in multiple applications, while creating an impact in terms of privacy. As face recognition solutions based on artificial intelligence (AI) are becoming popular, it is critical to fully understand and explain how these technologies work in order to make them more effective and accepted by society.

Signal Processing Engineer

Who we are looking for:
An experienced signal processing engineer who is creative, innovative, thrives on technical challenges, and is comfortable merging concepts from different technical disciplines.

Experience (required):
• 5 years of signal processing experience (analysis, modification, and synthesis)
• strong background emphasizing and detecting components in signals
• strong data analysis/data science abilities
• strong programming abilities

Post-doc in Biomedical Image Analysis

Job offer: Post-doc in biomedical image analysis

We have an opening for a post-doc in biomedical image analysis. We are searching for highly motivated candidates who are interested in working with us. We develop and integrate deep learning techniques into new algorithms to improve state-of-the-art processing and analysis of biomedical imaging data. This includes image segmentation, classification, filtering, but also image registration.

Our website : http://bia.riken.jp/jobs.php%3Flang=en.html

Job description

PhD Student

Position description: The research project will focus on developing machine learning/deep learning methods for fundamental computer vision problems including object motion tracking, segmentation, 3D reconstruction, classification and image captioning in 2D/3D images including RGBD images, remote sensing data, 3D CT/MRI medical images and biomedical text.

PhD Position in Digital Document Analysis

  • Digitization is an important means to preserve the content of materials which are basically vulnerable to physical damages. In particular, paper based (and especially historical) documents account for an invaluable source of information. The goal of this PhD project is to develop machine learning tools for analyzing scans of documents.
  • We are looking for a Master in Mathematics, Computer Science or Engineering with expertise or interest in image processing, machine learning and natural language processing in particular. Coding skills are required.

Postdoctoral Scholar

A postdoctoral scholar position with a focus on applications of machine learning in cardiac MRI. Details can be found at:

https://recruit.ap.uci.edu/JPF05862

Post-doc in Biomedical Image Analysis

We have an opening for a post-doc in biomedical image analysis. We are searching for highly motivated candidates who are interested in working with us. Keywords: Image Analysis, Deep Learning, Biomedical Image Processing, Artificial Neural Networks, Machine Learning. Visit our website for furhter details.

PhD Position in Efficient Very-Wide-Area ToF 3D Sensing by Means of Adaptive Compressive Sensing

A question that naturally arises in active sensing systems, such as ToF systems, is how much volume can be sensed with a given power budget, and how this can be extended by means of some more intricate sensing scheme. The main objective of this project is the development of a very-wide-area ToF 3D sensing system which has to be outstandingly efficient regarding the power consumption. To attain such an ambitious goal we propose bringing compressive sensing into the game and using recently proposed adaptive methods for constructing close-to-optimal binary sensing matrices.

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