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(postdoc) Limited contract until: 31.12.2029 Job ID: 5198 Explore and teach at the University of Vienna, where over 7,500 brilliant minds have found a unique balance of freedom and support. Join us if you’re
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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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-- 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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& Responsibilities: Develop advanced deep learning methods for radiology or pathology medical imaging Integrate imaging data with EHR, clinical notes, or genomic data Conduct research on segmentation, classification
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control framework for microfluidic live-cell analytics in close collaboration with partners at HZI, Helmholtz Munich and HHU. Your tasks in detail: Establish deep-learning–based segmentation, species
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George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș | Romania | 3 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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researchers with ample experience in MEG/EEG data analysis, BCIs, signal processing, deep learning for brain imaging analysis, biomedical statistics, dynamical systems and research on motor control. The lab has
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George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureș | Romania | 3 months ago
Research, 2024, 52(12):1-19. https://doi.org/10.1177/03000605241302304 Masumshah, R., Eslahchi, C. DPSP: a multimodal deep learning framework for polypharmacy side effects prediction, Bioinformatics
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languages, for example Python, and general purpose deep learning frameworks, such as Tensorflow or PyTorch; The interest and ability to share knowledge with other ESA organisational units. You should also
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programming (e.g., Python) Documented proficiency in deep learning frameworks (e.g., PyTorch) Documented background in machine learning, mathematics, linear algebra, and statistics Fluent oral and written