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data and archaeological signatures (e.g., circular mounds, linear ditches, rectangular foundations, etc.) tailored for AI applications – Feature engineering and representation learning to enhance
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 15 hours ago
The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early
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machine learning for audio and acoustics - Research in experimental techniques in acoustic -- - Investigación en procesamiento de señales y aprendizaje automático aplicados al audio y la acústica
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correlated materials. If time allows, as mentioned before, there is also the possibility to address LLM and chemistry with very similar approaches Where to apply E-mail [email protected]
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learning process . An excellent technical infrastructure, and on-campus children's day care. Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate. We
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for latent variables and their connections to modern machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates
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are needed to prevent infections with bacterial enteropathogens and AMR. Our previous research showed that there are differences in the E coli colonization of infants’ microbiome between LMIC vs. high-income
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learn new techniques and work across disciplines is essential, as is a focus on mechanistic understanding, reaction kinetics, and analytical methods. Candidates should have very good English communication
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and translational researchers, contributing to projects with tangible clinical relevance for children with cancer and immune disorders. Learn more about the Salzer Group . Your responsibilities Generate
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histopathology and clinical information to learn robust cross-modal representations for diagnostic prediction. It pursues two integrated objectives: (i) to develop generative and explainable AI approaches