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the application of these methods to problems in the physics of oxides, semiconductors, metals and their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists
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next generation of scientists and build a workforce equipped with expertise in integrating advances in biomedical engineering, technology, and Artificial Intelligence (AI) and Machine Learning (ML
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for scientific research English proficiency for the purpose of conducting classes (and/or consultations) in this language fluency in Polish language strong computer literacy and readiness to learn new software
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, machine learning, statistics and programming skills (R and Python) is preferred. Record of peer-reviewed publications. Knowledge in one or more of the following areas is desirable: single-cell profiling
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). The ideal candidate brings a strong machine learning foundation, curiosity about sound and music computing, and enthusiasm for collaborating with PhD students and postdocs. You will help combine individual
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with the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The research group on statistical models
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) equipped with a cryogenic stage for surface analysis - Develop computer code (e.g. Python) for the development and analysis of optical cavities Qualifications § PhD degree in Materials, Electrical
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ability to quickly learn and master computer programs. Strong analytical skills and excellent judgment. Ability to work under deadlines with general guidance is essential. Excellent organizational skills
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of formulating them, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment
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a Postdoc you are given/provided an opportunity to develop your independence as a researcher with the support of senior colleagues. The appointment is limited to 3 years and is full time in accordance