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Field
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of an algorithm to analyse discharge signals, building a secure dataset for new tool–workpiece material combinations enabling process-integrated antibacterial coatings, derivation of data-based strategies to reduce
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to evaluate and advance machine learning algorithms for one of the following application areas: “Characterising forests variations in relation to distance from pre-Columbian earthworks in the Amazon forest
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working with heterogeneous datasets originating from different sensors or data sources. Familiarity with forest ecology, biodiversity, or natural-resource applications. Experience developing reproducible
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(one) Research Grant (RG) under project “NANOCEAN - Nano optical sensors for rapid detection of marine toxins”, ref. n.º 16994, of CESAM - Centre for Environmental and Marine Studies. The complete
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fundamental physics of light-matter interactions, quantum optical effects in advanced sensing, and new applications of extreme sensors. Main Tasks and Responsibilities The Atomic Quantum Optics group studies
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with European industry. The EISLAB division at Luleå University of Technology conducts research in electronic systems design, sensor systems, cyber-physical systems, the Internet of Things and machine learning
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democratic, organisational, and societal values such as algorithmic fairness, transparency, explainability (XAI) into rigorous, quantifiable engineering metrics without sacrificing the general utility of said
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 1 month ago
the application of various ML algorithms, including supervised algorithms for environmental predictions, as well as unsupervised algorithms for clustering and LLMs/Transformers Experience with sequence data
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. The EISLAB division at Luleå University of Technology conducts research in electronic systems design, sensor systems, cyber-physical systems, the Internet of Things and machine learning, and works on
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through a mixture of qualitative, quantitative, and sensor based approaches to assess student experiences. Rather than seeking to “fix” individual students, our project challenges stigma by addressing