5 bayesian-inference-"Integreat--Norwegian-Centre-for-Knowledge-driven-Machine-Learning" positions in Luxembourg
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design, development, and optimization of scalable AI inference pipelines Implement and experiment with LLMs, GNNs, multi-modal AI, and vision models Apply techniques such as quantization, pruning
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methods for causal inference in observational data, is strongly preferred. Using various existing large datasets with rich information for knowledge synthetisation and triangulation over the course of the
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semantic can be inferred. At the end of the processing pipeline, a component will aggregate the accumulated knowledge about the discovered attack surface. The ultimate objective is to provide useful and
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MLOps, model deployment, or scalable data pipelines. Knowledge of advanced ML model serving frameworks (TorchServe, TensorFlow Serving, Triton Inference Server).Familiarity with AI risk assessment
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). The goals are to develop new computational methods that allow the scientific inference of explainable attributes to describe human behavior and self-reports as well as to make progress in the computational