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focus on the following areas: Subspace tracking and autoencoders for amplitude and phase noise characterization Bayesian filtering Building experimental set-ups for noise characterization Reinforcement
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research on the development of new inference methods and algorithms for wide classes of stochastic models. However, research will be conducted in collaboration with biologically oriented researches allowing
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the Universities of Vienna and Innsbruck as well as the IST Austria. Two PhD students will be working on cosmological inference using simulations with Oliver Hahn, and one PhD student will be working with Sylvia
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). o Software and hardware co-design. Indicative skills/experience: - Designing for real-time inference and hardware acceleration. - Knowledge of runtime reconfiguration, latency profiling, and dataflow
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the Universities of Vienna and Innsbruck as well as the IST Austria. Two PhD students will be working on cosmological inference using simulations with Oliver Hahn, and one PhD student will be working with Sylvia
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significant progress characterizing visual processing, we do not understand how the visual system solves visual inference problems in natural environments and we are still far from having a complete
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tracking, accelerometers or sEMG and are constrained by power consumption and limited robustness. MiNI introduces a multimodal approach that integrates electrical (sEMG), mechanical (ultrasound), and
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structural modeling, machine learning, and competitive intelligence to infer demand using partial or aggregated signals. While early results are promising, important questions remain: Which partial data is
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-supervised learning, and few/zero-shot techniques — the student will adapt models to ecological data. Bayesian deep learning and ensemble methods will be explored for trustworthy uncertainty estimation
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constrained and valid action space, enabling the generation of reliable answers in a single, efficient inference step. Third, an efficient Data-Driven Low-Rank Adaptation scheme will be employed to efficiently