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Field
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data to design robust, efficient deep learning algorithms that maximize the information extracted from images and delivered to the robot. To be successful in this role, we are looking for candidates
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environments, specifically Computer Vision, Machine learning algorithms and methods for rock characterization, fragmentation prediction, and mining optimization. Specific Requirements Good academic and
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findings regularly. Prototype algorithms, validate outputs, and document methods clearly Collaborate asynchronously with an international team, present findings regularly Experience with remote sensing
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completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
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into optimization frameworks. Study the theoretical foundations of randomized algorithms in the context of sparse tensor operations and apply them to real-world datasets. Parallel Computing: Explore opportunities
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Basic qualifications (required at time of application) PhD or equivalent international degree, or enrolled in a PhD or equivalent international degree granting program Additional qualifications (required
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2026 Is the job funded through the EU Research Framework Programme? Horizon Europe - ERC Reference Number 101161065 Is the Job related to staff position within a Research Infrastructure? No Offer
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) Country United Arab Emirates Application Deadline 13 Nov 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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structures, algorithms, and debugging tools; familiar with medical image processing (vtk and ITK), medical device communication protocol (OpenIGTLink) and open-source medical imaging computing platform like
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Inria, the French national research institute for the digital sciences | Lyon, Rhone Alpes | France | 14 days ago
28 Oct 2025 Job Information Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Researcher Profile Recognised Researcher (R2