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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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. This project aims to establish provable guarantees for Human-GenAI-Alignment by integrating statistical methods with adversarial methods. For example, by leveraging PAC methods and conformal prediction, we can
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research in the areas of forest planning, forest remote sensing, forest inventory and sampling, forest mathematical statistics and landscape studies. The department is also responsible for the implementation
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/2025.03.02.641062 Your Profile The successful applicant should hold a master's degree or equivalent qualification in computer science, statistics, mathematics, physics, and/or engineering, or a degree in biological
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Science, Statistics, Applied Mathematics, or a related field. • Strong background in convex analysis, statistical machine learning (reinforcement learning, LLM and generative modeling), stochastic modeling and
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the theory of optimization algorithms and high-dimensional statistics to address some of the most fundamental questions in ML such as the behavior of neural networks. The environment of this project is highly
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analysis, with possible specialisations in genomic and molecular biology techniques as well as in algorithms, statistics and artificial intelligence for molecular genetics. This is based on perspective and
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statistics. This position will be placed under Research Thrust 2 which mainly involves mathematical physics. Requirements: The applicants must have documented strong qualifications in functional and spectral
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of computer science, mathematics, applied mathematics or statistics. Applicants must demonstrate proficiency in machine learning or statistical modelling and have some experience with computing through Python, R or C
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university degree and a Ph.D. (or equivalent experience) in mathematics or a closely related field is required. Moreover, the candidate should be familiar with statistical/logical knowledge representation