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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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machine learning-based model to map satellite retrievals to ground based air pollutant concentrations Conducting error assessment on the derived concentration data Implementing new observational data
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and research interests, the project may focus on one or more topics such as cyber risk assessment, human–machine interaction, cyber risk communication, cybersecurity training, or organizational and
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childhood through adolescence. Data collection will primarily take place in schools, where children and adolescent research participants will complete computer-based learning tasks designed to capture
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Hey machine learning enthusiast with a love for physics and
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scholarship holder in the field of Machine Learning and Systems Modelling for Advanced Air–Liquid Interface Cell Culture Models. Position You will work actively on the preparation and defence of a PhD thesis
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expertise in biodegradable drug-delivery technologies (https://www.mgshell.com ) and Politecnico di Milano’s strengths in microfabrication and digital machining. Secondments include MgShell (3 months
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check the minimum entry requirements for the PhD . Applicants must also satisfy Monash’s English Language Proficiency requirements; Demonstrate knowledge of machine learning, medical image analysis
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closely related discipline). You have a strong interest in AI/machine learning, data mining, regression analysis, responsible AI, causal inference, and programming (R/Python, SQL). Moreover, you are driven
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, machine learning, explainable artificial intelligence (XAI), digital twins, and integrated data-model approaches. • Study of the frugality of the developed approaches by reducing the requirements