66 phd-in-architecture-interior-design-built-environment Postdoctoral positions in Switzerland
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of antimicrobial coatings. This project aims to tackle the growing challenge of infections caused by resistant bacteria through designing innovative, non-toxic, and durable antimicrobial solutions. This highly
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of infections caused by resistant bacteria through designing innovative, non-toxic, and durable antimicrobial solutions. This highly interdisciplinary project will be conducted in collaboration with several Swiss
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industrial partner, you will design and implement innovative architectures for real-time detection and control of laser processes. This interdisciplinary role combines artificial intelligence and machine
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will work on projects related to the future of sustainable asphalt pavements, including conceptualizing, designing, producing laboratory mixtures, testing, and supporting the implementation of innovative
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research. Your position Design, implement, and evaluate large-language-model (LLM) pipelines for synthetic data (fine-tuning, retrieval-augmented generation [RAG], prompt engineering). Plan and analyze
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demonstrating how an existing anti-biofilm compound binds to and activates its specific target in Pseudomonas aeruginosa, we will design, develop and test novel anti-biofilm compounds with broader activity
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. Empa is a research institution of the ETH Domain. Empa’s Laboratory Materials for Energy Conversion focuses on materials and device innovation for sustainable energy conversion and storage technologies
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Preparation of results for publishing and presentation at conferences Development of models for health and environmental impact assessment Your profile We are looking for a candidate with: PhD in the area of
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activates its specific target in Pseudomonas aeruginosa, we will design, develop and test novel anti-biofilm compounds with broader activity against the WHO priority pathogens Escherichia coli, Klebsiella
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-collection costs while maintaining validity for marketing and opinion research. Design, implement, and evaluate large-language-model (LLM) pipelines for synthetic data (fine-tuning, retrieval-augmented