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machine learning and AI methods to develop clinical decision support systems for high-flow nasal cannula therapy. The project: Concerns around the possible negative consequences of delayed escalation
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high-density 3D Multi-Electrode Arrays (3D-MEAs) — done in close collaboration with experts, providing full support as you learn to analyze this data. o Multi-Omic Profiling: Performing and analyzing
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, Environmental Sciences, Chemical Engineering, Data Science, Geology, or related disciplines. Specific Requirements Programming skills interest in machine learning techniques. Ability to work in an organised
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and hardware security assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and
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Institute of Technology The Łukasiewicz Research Network – Krakow Institute of Technology (https://kit.lukasiewicz.gov.pl/ ) is a member of the Łukasiewicz Research Network (https://lukasiewicz.gov.pl/en
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Machine Learning within the School of Medicine at the University of Limerick. This is a methodologically focused PhD for candidates with strong quantitative backgrounds who wish to develop novel statistical
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Kurt Schwabe Institute for Measuring and Sensor Technology e.V. (KSI) Meinsberg | Waldheim, Sachsen | Germany | 3 months ago
- Contribution to a machine-learning-driven experimentation platform - Comparison of automated and manual experiments Your profile: - Master’s degree in chemical engineering, biotechnology, chemistry, physics
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is seeking a PhD student to join an ongoing Royal Society–Research Ireland University Research Fellowship project focused on Machine Learning for the Design of Additively Manufactured Two-Phase Heat
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of compensating for nonlinear PA characteristics under dynamic operating conditions. Advanced machine learning and neural network approaches will be explored to improve linearization performance while reducing
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Health Data Sciences and Informatics (OHDSI, https://ohdsi-europe.org ) initiative, dedicated to bring out the full value of observational health data through the OMOP Common Data Model and large-scale