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Field
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insights that inform biodiversity management. The project includes: · Apply of deep learning models to annotate bird and bat species from sound recordings. · Develop a Bayesian statistical
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examines how digital twins, simulation-based multi-objective optimization, and AI-driven decision support systems can enable manufacturing industries to maintain stable production, optimize energy usage, and
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-ecological sustainability and value considerations can be systematically integrated into early-stage design and decision-making. The research objective is to support the acceleration towards sustainable and
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decoupling the key thermoelectric characteristics using a “nanoparticle-in-alloy” design strategy within an inorganic thin-film system. The core objective of the research is to tailor the interface structure
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, a cutting-edge research initiative in forest ecology, biodiversity monitoring, and molecular methods. The project explores how airborne environmental DNA (eDNA) can be used to detect and monitor
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ecology, biodiversity monitoring, and molecular methods. The project explores how airborne environmental DNA (eDNA) can be used to detect and monitor migratory species, invasive species, and pathogens
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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theories from tractable models (probabilistic circuits) and Bayesian statistics to tackle the reliability of machine learning models, touching topics such as uncertainty quantification in large-scale models
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structured communication can support shared decision-making and enhance treatment adherence. Key objectives include: Analyzing and Prevention. Conduct empirical research to: Assess how changes in survival
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inform more targeted prevention strategies. Key objectives include: Analyzing Inequality and Prevention: Conduct empirical research to assess how prevention policies and healthcare innovations affect