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Field
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part of a team Understanding of dynamical systems, time series models, machine learning, Bayesian statistics, experience in handling environmental and climate data is a merit We offer: This position is
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. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set
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exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised
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Max Planck Institute for Gravitational Physics, Potsdam-Golm | Potsdam, Brandenburg | Germany | about 1 month ago
(Einstein Telescope), acceleration techniques for gravitational-wave inference, including machine learning, cosmography with gravitational waves (including dark energy, dark matter, gravitational lensing
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analytical skills for model formulation and optimization Demonstrated research potential, ideally with a track record of publications in relevant venues (journals such as IEEE T-ITS, INFORMS Transportation
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to avoid sources of bias such as the target trial approach (www.bips-institut.de/en/research/cross-departmental-working-groups/working-group-gepard-target-trials-for-causal-inference-gettcausal.html ) and
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software (e.g., LLM agents for finding and fixing bugs) Static and dynamic program analysis (e.g., to infer specifications) Test input generation (e.g., to compare the behavior of old and new code via
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to reason about software (e.g., LLM agents for finding and fixing bugs)Static and dynamic program analysis (e.g., to infer specifications)Test input generation (e.g., to compare the behavior of old and new
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dendritic cells can be associated with distinct gene expression patterns and immunological outcomes. However, current interactome analyses have so far been limited to inferred interactions based on cellular
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coherence, optical), including cross-modal fusion and modality distillation • Design a causation analysis framework combining deep learning with causal discovery & inference to quantify the influence