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Research Infrastructure? No Offer Description The Department of Physics at Chalmers University of Technology invites applications for a postdoc position targeting machine learning in optics. In this position
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hold, or be close to completion of, a relevant PhD/DPhil in one of the following subjects: computational genomics, genetic or molecular epidemiology, medical statistics or statistical genetics. You must
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data. A core technology leveraged by researchers at the center is deep machine learning, targeting the development of innovative tools and concepts in both the area of molecular biology and the field
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applying machine learning or other elements of artificial intelligence to solving significant scientific or engineering problems Interest in software development, with particular emphasis on the Python
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of quantum computing, quantum algorithms and complexity, quantum cryptography, quantum program verification, quantum machine learning, etc. * Within the predetermined research scope and methodology, conduct
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terms of research and education, covering all aspects of computer science, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering
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skills (e.g. Python, Julia) to merge concepts of chemical engineering, operations research and computer science, as you may also need to deploy machine learning to support data analytics and complex
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The Department of Physics at Chalmers University of Technology invites applications for a postdoc position targeting machine learning in optics. In this position, you will become part of a
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dynamics, quantum mechanical simulations, and machine learning. Proficiency in programming languages and computational software’s. Strong motivation and passion for research in the field of sustainable
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Université de Bordeaux / University of Bordeaux | Villenave d Ornon, Aquitaine | France | 14 days ago
genomic diversity and genetic load from available genomic data for various woody species. For this, you will use the available pipelines from phase 1 of the project and apply machine learning-based methods