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Field
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comparing supervised and unsupervised methods (e.g., regularized regression, tree-based models, ensemble methods, clustering, dimensionality reduction) and deep learning approaches Developing and applying
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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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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 3 months ago
PhD in computer science, mathematics, physics, bio-/medical informatics or related fields, specializing in image analysis or machine learning, proficiency in deep learning techniques (CNN, VIT
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knowledge of deep learning and computer networking/systems is required Experience with AI as a platform and expert use of AI as a tool strongly desired Experience implementing a language model is a plus Refer
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advancing social justice, equity, and civic engagement. Qualifications Basic qualifications (required at time of application) PhD (or equivalent international degree) or enrolled in a PhD (or equivalent
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. The Regenerative Immunology lab is currently composed of three PhD students, three postdoctoral fellows, one MS student, and one animal technician. The lab resides within the Division of Molecular Medicine and Gene
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific vision
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) signal processing, machine/deep-learning and computational linguistics. The team mobilizes them to produce methodologically sound research in response to some of the challenges posed by the nature and
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mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us
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imaging, deep proteomics, metabolomics, metaproteomics, and machine learning (ML) approaches to develop diagnostic classifiers, spatial tissue atlases, and identify potential therapeutic targets