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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
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advanced AI algorithms to optimize and understand the optical properties of light-trapping surfaces. (more information can be found in the following News post ). You will work closely with colleagues both
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attacks Develop and implement ML algorithms to identify vulnerabilities and predict potential threats in supply chain systems Prepare project deliverables and disseminate results through high-quality
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the majority are international and the working language is English. The research group of Mikkel H. Schierup currently employs six postdocs and six PhD students in a variety of projects in evolutionary genomics
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programming of algorithms. The use of programming languages such as Python, R, SQL, and C++ will be a daily part of the project, and proficiency in these languages is required. However, additional datasets will
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skills in data analysis, machine learning, as well as in mathematical and computational modelling? You will have the opportunity to investigate innovative solutions using machine learning algorithms and
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the project. Disseminate research findings through publications and presentations at international conferences. Expectations of qualifications: PhD in Ecology, Evolutionary Biology, or a related discipline
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classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence
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strong interest in developmental, cellular or molecular neurobiology, in link with human brain evolution and diseases. PhD in neuroscience, genetics, evolutionary biology or related field. Experience in
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with