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Field
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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
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genome-resolved multi-Omics methods, statistical/metabolic modeling, and machine learning. The postdoc will apply these approaches to generate a systems-level understanding of microbiomes including
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learning methods development and application. The postdoc associates will be exposed to rich multi-omics data, a variety of diseases, advanced statistical and machine learning methods and wide collaborations
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using deep learning or causal learning methods. Candidates must have solid experience with large spatial and temporal datasets, large model manipulation, and HPC. The candidate must also have experience
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George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș | Romania | 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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perception systems, using deep learning and simulation-to-real domain adaptation techniques. You will work with a multidisciplinary team, contributing to fundamental and applied research. Your role will
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engineeringEducation LevelPhD or equivalent Skills/Qualifications We are seeking a scientist with: Expertise in image-based biological tissue modeling and simulation Good command of deep learning Expertise in coding and
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, OpenPose, homography estimation, optical flow). Experience with eye-tracking data collection or analysis. Familiarity with deep learning frameworks (PyTorch, TensorFlow). Experience working with multimodal
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-- Deep learning for nuclear physics -- Effective field theory for nuclear structure -- Hard Probes of Quark-Gluon Plasma -- Hot and cold lattice QCD -- Physics in electron-ion collisions -- Relativistic
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and development of perception stacks for autonomous mobile systems in general in any field Machine learning/deep learning experience applied to perception and any experience with deep Learning