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Field
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candidates will be required to fund the difference between the home fees and international fees. Overview Are you interested in developing Brain-Computer Interface technologies to control brain stimulation in
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on developing the simulation models, data models and algorithms required to enable connected cross-disciplinary design and optimisation, laying the foundations for more integrated and intelligent engineering
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participation of citizens. You will focus on developing adaptive learning systems that enhance the transparency and contestability of AI decisions through personalized, multimodal explanations. Your job AI is
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usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and algorithms for resource-efficient
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-of-information metrics. Propose computational algorithms to estimate these metrics. Design and execute simulation studies to evaluate the above. Develop and test statistical software. Write user-friendly guidance
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Hands-on experience with machine learning algorithms and developing/testing complex software systems Basic understanding of hardware/embedded software development Additional Requirements (MSCA Eligibility
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development experience, and simulation/prototype development experience in MATLAB, Python, and/or OCTAVE and experiences in system level simulation and testing. Focus on problem solving and self-motivated
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The Quantitative Genetics research group is interested in developing statistical genomics toolboxes to decipher the genetic architecture of important crop traits, such as grain yield, adaptation
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 1 month ago
scientists developing computational simulations in various fields will be encouraged, in particular with the Gysela code, which is part of the ITER project. The project will first focus on dimensionality
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical