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Field
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strategies. Duties and Responsibilities Design, implement, and evaluate deep learning models for spatiotemporal data, with an emphasis on medium-scale foundation models. Leverage model embeddings in causal
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in image processing and analysis, including deep learning (e.g., CNNs) experience with correlative imaging workflows and 2D/3D registration techniques strong programming skills in Python and/or C/C
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and obsessive-compulsive disorders), and to optimise neuromodulation therapies such as deep brain stimulation. The team combines intracranial recordings and EEG, brain imaging, brain stimulation
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. Candidates whose doctoral work focused on deep learning methods and who have a strong interest in genomics will also be considered. Experience: At least one publication in computational genomics or machine
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venuesStrong programming skillsSolid mathematical foundation, including linear algebra, probability, statistics, and optimizationBroad and in-depth experience with machine learning algorithms and deep learning
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Mentor and Advise PhD thesis within the research group. Current topics include, reinforcement learning for tactile-aware manipulation skills, tactile sensing, mechanical intelligence for grasping, and
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decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model
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The Department of Ecoscience at Aarhus University invites applications for two postdoctoral positions to strengthen our research on image recognition, computer vision and deep learning applied
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at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will
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and integration of multimodal neuroimaging, behavioral and clinical data, and building large-scale deep learning models for multimodal neuroimaging datasets to construct predictive network models in