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technologies including semantic/task-oriented data processing, signal processing, and network resource management to improve the performance of future wireless communication systems. Finally, due to the large
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methods You will work closely with: - Dr. Martin Ramacher (machine learning for environmental applications) - Dr. Matthias Karl (urban air quality modelling and emissions) and collaborate within a project
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. What you bring We are looking for candidates with: A PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or a closely related field; A strong publication record in
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and algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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. Author scientific publications, present findings at conferences, and contribute to patents or technical innovations. Qualifications: PhD in Artificial Intelligence, Machine Learning, Data Science
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the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy to consider candidates in one of the two fields who can demonstrate a strong basis for working in this cross
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 2 months ago
across European life-science AI efforts. Requirements PhD in computational biology, bioinformatics, machine learning, or a related computational field Hands-on experience with foundation models / large
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migraine and other headache disorders, neuroepidemiology, and other neurodegenerative disorders. We use advanced biostatistical modeling, artificial intelligence (AI), machine learning, and neuroinformatics
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and