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Field
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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, and spatial transcriptomics. Key responsibilities include: Developing AI/ML methods for image alignment across modalities Automated feature detection Predictive modeling of vascularization patterns
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management. Our research is problem-oriented and empirical, with strong emphasis on industry and societal relevance. Sustainability needs , digitalization, information technology developments and automation
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relevance. This position will be anchored at Communication, Control & Automation Section at ECE and will be supervised by Professor Qi Zhang, whose research focuses on Edge Intelligence, Goal-oriented
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, transport options and CO2 sources, as well as data on local regulation, (market ready) technologies and associated costs. The aim is to develop a (semi-)automated data acquisition workflow that will allow
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crosslink highly diverse bioeconomic production systems and to control them in a knowledge-based, adaptive, and largely automated manner. We conduct research in dialogue with society - knowledge-motivated and
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: Practical experience with real-time power system monitoring tools, PMU infrastructure, and automated disturbance detection systems. Publications/Research: A strong record of academic publications, technical
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itself evolves in an era dominated by data and automation. Your role and goals As a Doctoral/Postdoctoral Researcher, you will play a central role in the research project exploring the impact of AI
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: Leveraging LLM / VLMs for interdisciplinary problems, such as: AI-driven scientific discovery, automating hypothesis generation in finance / natural sciences / physical sciences, enhancing collaborative
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, developmental constraints and evolvability; knowledge of methods for automated phenotyping during development (e.g., for gene expression and morphology). Dynamical systems: knowledge of linear and nonlinear