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that span and integrate multiple scientific domains. We are looking for candidates with strong expertise in their respective fields and a passion for interdisciplinary collaboration. Each project will not
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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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working with high performance computers (e.g., parallelizing and distributing code). Experience in distributed data management and workflow systems. Preferred Competencies Ability to work independently and
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that this position will start in Fall/Winter 2025. Diversity code CWI encourages a diverse workforce: we endeavour to develop talent and creativity by bringing people from different backgrounds and
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take-home assignment to be discussed in a second interview. Please note that this position will start in Fall/Winter 2025. Diversity code CWI encourages a diverse workforce: we endeavour to develop