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Science About the project This PhD project integrates pharmacoepidemiology, causal inference, and machine learning to study real-world treatment patterns, effectiveness, and safety of monoclonal antibodies
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Education/Technology, Computer Engineering, OR a closely related discipline with related computer coursework AND related industry certifications OR Master's Degree and 18 graduate credits hours in a Computer
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow
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Python Arduino and C++ (Physical Computing) Creation of interactive objects and components Machine Learning and Natural Language Processing Specific Requirements Candidates must hold a PhD in engineering
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | 2 days ago
Degree Doctor of Philosophy (PhD) in Heritage Studies Course location Cottbus Teaching language English Languages All courses are taught in English. The dissertation is written in English, and the
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. 132, no. 3, pp. 1521–1534, 2012. [6] S. Koyama, J. G. C. Ribeiro, T. Nakamura, N. Ueno, and M. Pezzoli, “Physics-informed machine learning for sound field estimation: Fundamentals, state of the art, and
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, dimensionality reduction and/or machine learning methods (e.g., Lasso, ridge regression) is highly desirable. Familiarity with neurostimulation, Parkinson’s disease, or neuropsychological assessment tools is
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Mobility Engineering (Naval Architecture – Marine Technology profile) and we are part of the Nordic Master in Maritime Engineering. The research group consists of senior researchers, post-doc and PhD student
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-order modeling, or machine learning Experience collaborating in interdisciplinary research teams What you will do Develop hybrid quantum–classical methods to improve simulation and prediction
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(iii) complex architectures with tightly coupled components hinder modular adaptation. To address these limitations, we research a physics-guided machine learning framework that integrates physical