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species like birds and bats. Combining PAM with occupancy modeling allows for large-scale ecological studies, assessing both individual species and community responses to environmental changes. In this 4
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of implant surfaces functionalised with antimicrobial peptides (AMPs) using experimental infection models to assess the biocompatibility and therapeutic potential of AMP-coated titanium and polymers in
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conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in large language models (LLMs
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flow, including circuit design, simulation, layout, PCB design, and measurement of integrated circuits using advanced CAD tools. Excellent communication skills and proficiency in both written and spoken
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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to either first-principles calculations or AI supported database management of high-throughput type calculations/simulations. Basic knowledge of density functional theory (DFT) is beneficial. Strong
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to this third-cycle studies which corresponds to four years. Position description We are seeking a PhD student to join our research team specializing in the analysis and modeling of multiphase flows. The research
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references A copy of the doctoral degree certificate Where to apply Website https://lu.varbi.com/en/what:job/jobID:851159/type:job/where:39/apply:1 Requirements Research FieldBiological sciencesEducation
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level in electrical engineering, electromagnetic engineering, wireless engineering, engineering physics, applied physics, a closely related field. Good command of electromagnetic simulation tools such as
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this, we focus on self-supervised denoising, where models learn to restore images using only the noisy data itself — without requiring clean references. Existing approaches often rely on convolutional neural