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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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are eligible to apply. Personal skills We are looking for highly motivated and structured candidates with excellent laboratory skills and excellent collaborative qualities. The successful candidate is positive
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or her in the top tier. This requirement also applies to the master’s thesis. Personal skills The person hired must: be able to work independently and in a structured manner have good collaboration skills
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structured candidates with excellent laboratory skills and excellent collaborative qualities. The successful candidate is positive-minded, well organized, looking forward to learning and developing new
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Additional preferred expertise and experience Experience with longitudinal data analysis and advanced statistical methods (e.g. linear and generalized mixed-effects models, growth curve analysis and structural
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statistical methods (e.g. linear and generalized mixed-effects models, growth curve analysis and structural equation modeling). Familiarity with the opioid and endocannabinoid system and topics related
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and other forms of dissemination. An additional important task is to contribute to the establishment of a harmonised data management structure and liaison with the international partner sites and
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research across sciences, medicine, social sciences and humanities. The university seeks to further strengthen this by facilitating greater collaboration, with a centre structure that can accommodate both
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during sitting, walking, running or performing sports; or (ii) generative machine-learning models for physiologically realistic avatar construction from image or point-cloud data. Qualifications
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within 3 months after the start of the research project leading to the Ph.D. degree. Personal skills Ability to work independently and in an interdisciplinary team Well-structured approach to work and time