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Veterinary Medicine ● Variant discovery and genome annotation: Apply deep learning and graph-based models to improve variant calling, transcriptome annotation, and functional prediction in veterinary-relevant
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supporting better patient outcomes. The successful candidate will lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning
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George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș | Romania | about 2 months ago
). Label-driven / weakly-supervised CNNs for multimodal deformable registration (arXiv / MICCAI threads) — key papers showing deep learning approaches for fast, deformable registration. https://doi.org
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lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning techniques—including vision-language architectures (e.g., CLIP
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. Applications are invited for a full-time Postdoctoral Researcher in development of plasmonic nanopore for single molecule sequencing by surface enhanced Raman spectroscopy (SERS) position under supervision by
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, and recommend personalised treatment for musculoskeletal conditions. Key skills will include deep learning, convolutional neural networks and transformers, multimodal AI and fusion modelling. Imaging
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CORE A*/A conference paper. We invite applications for a postdoctoral position focused on the development of predictive models for clinical outcomes following Deep Brain Stimulation (DBS) in Parkinson’s
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Number: 6843258 Postdoctoral Research Scholar 0-5 yrs Experience Req No: 2025-19601 Category: Research/Project Type: Post Doc Salary: $5,858.67 Close Date: Overview This position has been budgeted
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
hybrid models that integrate limnological knowledge into machine learning models following the paradigm of Knowledge-Guided Machine Learning (KGML). The position is part of an on-going project
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risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include