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-compliance in ecological momentary assessment or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models in small samples. The appointment is a full-time
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are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish
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where AI systems are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s
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Norwegian courses. Required selection criteria You must have completed a doctoral degree in (machine learning, statistics, or similar). You must have a professionally relevant background in algorithms
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in coding e.g. Python, or MATLAB Writing skills for research papers. Experience in applied machine learning, fault diagnosis, laboratory testing and development is a plus. Good knowledge in Norwegian
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competencies that the PhD fellow should acquire, no later than one month after commencement of the fellowship period. The department is responsible for ensuring that the plan is followed up and that the PhD
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completed a doctoral degree in (machine learning, statistics, or similar). You must have a professionally relevant background in algorithms, machine learning, database systems, or data mining. Experience with
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, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish an internationally
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for imaging Apply for this job See advertisement About the position Position as PhD Research Fellow in machine learning available at Department for Informatics with the research group Digital Signal Processing
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Mechatronics.. Criteria for positions as PhD Research Fellow (in Norwegian). Desired qualifications Good experience/knowledge in electrical machines, synchronous generators, applied signal processing, applied