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- 10 Additional Information Benefits Work in a dynamic group. Eligibility criteria Good command of English. Knowledge in project field. Selection process Please see https://resurseumane.ase.ro/non
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of English. Knowledge in project field. Selection process Please see https://resurseumane.ase.ro/non-gaussian-self-similar-processes-enhanci… Website for additional job details https://resurseumane.ase.ro/non
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-omics liquid biopsy data for minimal residual disease (MRD) detection, quantification, and assessment. This project will involve applying and evaluating statistical and machine learning models for data
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Biotechnology and Bioprocesses, Membrane Technology, Chemicals and Materials, Resource Recovery, and Modelling & Artificial Intelligence. By harnessing its cross-cutting, interdisciplinary, and transformative
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answer maps accordingly. We use knowledge graphs to model these transformations and apply AI methods to scale them up across large map repositories, enabling users to explore many ways maps can be reused
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relies on accurate models of how the system interacts with its environment. Traditionally, such world models—comprising transition and observation models—are specified by domain experts, based
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authorized to work in the United States. The University is unable to work sponsor for this role, now or in the future MINIMUM QUALIFICATIONS Knowledge and skills required for this position are normally
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studying vascular biology, pancreas physiology or diabetes • Knowledge and Experience with programming, signal processing, or numerical modelling • Knowledge and Experience working with rodent animal
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: 281099154 Postdoctoral Research Associate D-26-ENE-00008 | Research | Andlinger Center for Energy and the Environment The Energy Systems Analysis Group (https://acee.princeton.edu/research/energy-systems
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Description Completion of doctoral thesis related to: Process and analyze experimental data. Develop predictive models using deep learning. Train, validate, and optimize neural networks (CNNs, etc.) applied