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of these methods to problems in the physics of oxides, semiconductors and their surfaces. Machine learning methods will be used to close the complexity gap. Applicants will have outstanding achievements or show
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develop new security definitions which match practical applications, explore complexity-theoretic relations, develop novel, sophisticated proof techniques, and design schemes that provably satisfy strong
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of technology and society. • Ability to work independently and as part of a research team in a collaborative environment. • Proven ability to manage complex projects, prioritize tasks, and meet deadlines
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. Leadership and management competencies. Ability to work in interdisciplinary teams. High sense of responsibility. Desirable qualifications are: Ability to analyze complex longitudinal data, e.g. using
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neural networks. The key challenge? Designing robust and stable numerical schemes that remain efficient even in high dimensions, effectively pushing back against the curse of dimensionality. The ideal
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. The goal is to design innovative assimilation schemes using nonlinear approximation tools—such as neural networks, spline functions, or Gaussian random fields. The core challenge? Developing methods
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postsocialism and its complex temporalities, as it reflects on the socialist past, responds to current realities, and envisions potential futures. On the one hand, postsocialist performance is analysed in