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Field
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will collaborate with a fellow PhD candidate and a postdoctoral researcher on integrating differential privacy into the generative pipeline, balancing privacy guarantees against data utility. You will
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statistical approaches to characterise disease trajectories, develop risk prediction models, and identify factors associated with differential treatment outcomes. The findings will improve understanding of long
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new approach has emerged that integrates data and mathematical models through neural networks. This has led to the development of a method for solving partial differential equations (PDEs) known as
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-type-specific pathomechanisms and intercellular crosstalk that underlie retinal vulnerability in AMD. Your Project As DC1 (Doctoral Candidate 1) within Pandora, you will: Differentiate iPSCs from
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expressed in the brain. They serve as auxiliary subunits of voltage-gated calcium channels and are important regulators of synaptic functions, including synapse formation, synapse differentiation, and trans
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will consider techniques like flow matching, and use ideas from optimal transport and neural (stochastic) differential equations, invariant Kalman filtering and geometric numerical integration
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mathematical arguments, and a drive to improve these skills. Curiosity about chaos, nonlinear dynamics, and unpredictability. Solid foundations in linear algebra and differential equations. Proficiency in
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related field; a solid understanding of molecular genetics, glial and myelin biology and rare neurological diseases; experience with either iPSC culture and differentiation or programming (Python and/or R
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transport and neural (stochastic) differential equations, invariant Kalman filtering and geometric numerical integration. The application areas will be chosen among the use cases of the aiD canter
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scientific knowledge, such as physical laws, differential equations, and domain-specific constraints, to model, simulate, and understand complex systems. The project will explore modern SciML methods