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language processing or computational linguistics; alternatively, in computer science or machine learning with a specialization in natural language processing Documented knowledge of core machine learning methods and
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to machine learning algorithms in order to get uncertainty estimates for parameters governing the distribution of the observed data. The predictive Bayes scheme for uncertainty quantification contains a wide
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for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defence are eligible for appointment Strong programming and artificial intelligence/machine learning skills Interest
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four years are expected to acquire basic pedagogical competency in the course of their fellowship period within the duty component of 25 %. Place of work is Department of Chemistry at Blindern/Gaustad
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Science About the project This PhD project integrates pharmacoepidemiology, causal inference, and machine learning to study real-world treatment patterns, effectiveness, and safety of monoclonal antibodies
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the areas of stochastic analysis and computational methods towards machine learning with focus on risk-sensitive decision making and control. Techniques may include forward, backward stochastic differential
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and development of PhD candidates and postdoctoral fellows, including individually tailored career development plans with formal supervision and project-based learning. Secondments, consortium meetings
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design and in silico validation intimately connected to experimental validation. In this project, you will develop machine learning methods and apply them in an interdisciplinary environment spanning
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. Qualification requirements A PhD degree within neuroscience, psychology, medicine, machine learning or biology or equivalent. Doctoral dissertation must be submitted for evaluation by the closing date
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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 leading