Research Position For Computer Vision Project

Organisation/Company Consejo Superior de Investigaciones Científicas (CSIC) Department Instituto de Óptica "Daza de Valdés" Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions Master Positions Application Deadline 1 Sep ****:59 (Europe/Madrid) Country Spain Type of Contract Other Type of Contract Extra Information ndefinite-term contract for scientific-technical activities, based on article 23 bis of Law *******, of 1 June, on Science, Technology and Innovation, and other regulatory regulations. Job Status Full-time Hours Per Week 37,5 Offer Starting Date 1 Sep **** Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? NoOffer DescriptionThis position entails working in the project V4NN, a collaborative project funded by the Fundamentos Research Program of the BBVA Foundation. This program aims to support exploratory research into central or foundational questions within a scientific field or at the intersection of several disciplines. Within the area of Mathematics, Statistics, Computer Science, and Artificial Intelligence, the project "Harnessing Vision Science to Overcome the Critical Limitations of Artificial Neural Networks (V4NN)," co-directed by Marcelo Bertalmío of the Spanish National Research Council (CSIC), was selected in the last call for proposals.The fundamental question this project addresses is how to overcome the most critical limitations of artificial neural networks (ANNs), limitations that can be characterized as an inability to emulate basic human vision skills.Despite significant advances in deep learning-based computer vision systems, many limitations still exist. The main objective of this project is to advance the state of the art of deep learning-based computer vision systems, improving their accuracy, robustness, efficiency, and providing better explainability. We target the core of deep neural networks and propose a new bio-inspired artificial neuron model, improving the building blocks of deep learning systems.The tasks to be performed with the contract are the following:Design new components for ANNs using cutting-edge vision science findings and techniques that go beyond the standard model.Optimize ANN components using key experimental results from visual psychophysics as training data.Validate and fine-tune the new ANNs for core computer vision problems.Write scientific articles and present at conferences.RequirementsResearch Field All Education Level Master Degree or equivalentSkills/Qualifications- Masters degree in Data Science, Computer Science, Artificial Intelligence, Mathematics, or related discipline.Specific Requirements- Experience in programming artificial neural networks.Languages ENGLISH Level ExcellentAdditional InformationSelection process#J--Ljbffr

Research Field All Education Level Master Degree or equivalent, Masters degree in Data Science, Computer Science, Artificial Intelligence, Mathematics, or related discipline. Specific Requirements

• Experience in programming artificial neural networks. Languages ENGLISH Level Excellent

The fundamental question this project addresses is how to overcome the most critical limitations of artificial neural networks (ANNs), limitations that can be characterized as an inability to emulate basic human vision skills. Despite significant advances in deep learning-based computer vision systems, many limitations still exist. The main objective of this project is to advance the state of the art of deep learning-based computer vision systems, improving their accuracy, robustness, efficiency, and providing better explainability. We target the core of deep neural networks and propose a new bio-inspired artificial neuron model, improving the building blocks of deep learning systems. The tasks to be performed with the contract are the following: Design new components for ANNs using cutting-edge vision science findings and techniques that go beyond the standard model. Optimize ANN components using key experimental results from visual psychophysics as training data. Validate and fine-tune the new ANNs for core computer vision problems. Write scientific articles and present at conferences.

This position entails working in the project V4NN, a collaborative project funded by the Fundamentos Research Program of the BBVA Foundation. This program aims to support exploratory research into central or foundational questions within a scientific field or at the intersection of several disciplines. Within the area of Mathematics, Statistics, Computer Science, and Artificial Intelligence, the project "Harnessing Vision Science to Overcome the Critical Limitations of Artificial Neural Networks (V4NN)," co-directed by Marcelo Bertalmío of the Spanish National Research Council (CSIC), was selected in the last call for proposals.