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-Geometric Foundations of Deep Learning or Computer Vision KTH Royal Institute of Technology, School of Engineering Sciences Job description The Department of Mathematics at KTH welcomes applications for a
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electrocatalytic processes. This postdoctoral position will investigate pulse-mediated electrodeposition of metal particles using deep eutectic and organic solvents, ultimately aiming to induce kinked high-index
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Join us at the forefront of life science AI. We are looking for a postdoctoral researcher to develop cutting‑edge, multimodal transformer‑based deep learning methods to extract insight from genomic
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biological markers and state-of-the-art deep learning, the research will uncover conserved cellular state transitions and perturbation response programs across biological systems. The successful candidate will
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on animal behaviour and welfare in pigs, where behavioural science is integrated with artificial intelligence and deep learning for the assessment of animal welfare. You will have a central scientific role in
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the following areas: deep learning, reinforcement learning, imitation learning, robot perception, navigation, and manipulation. Experience with whole-body control, humanoid or multi-DOF platforms, and
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/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle delivery. The postdoc will join Professor Nathaniel R. Street’s team at UPSC, working closely with
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, Micro-C/Hi-C, BS-Seq/EM-Seq), massively parallel enhancer assays (ATAC-STARR-seq), and comparative/bayesian/deep-learning analyses, with functional validation in spruce via CRISPR-Cas9 and nanoparticle
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related to staff position within a Research Infrastructure? No Offer Description Description of the workplace Automatic Control is an exciting and broad subject, covering both deep mathematics and hands
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep