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Field
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods
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of auditory stimuli and creation of stimulation sequences 5) Implementation of pilot studies in adults and infants 6) Programming analysis algorithms for FFR, MMN and statistical learning, based on spectral and
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across time and contexts. Job Description: You will develop and apply mathematical models and machine learning algorithms to analyze the structure and evolution of knowledge systems across different
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development, and computational algorithms A solid understanding of theoretical chemistry and solvation models such as COSMO The ability to work independently as well as collaboratively within a
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collaborators. Your work will develop algorithms, inference methods, and frameworks to adapt models from training data to test environments, which is necessary to resolve distribution shifts, hidden confounders
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econometric modeling, scientific-oriented thinking, communication and team skills, with a PhD in the field of economics, (business) mathematics, (business) informatics, marketing, management. As a member of the
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of the results to a broader audience of policy makers and society as a whole. Your experience and profile: Formal requirements: Applicants must have, or be close to obtaining, a PhD degree in Actuarial Science
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principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several national and international research groups, edits one of the major
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requirements and such other tasks that are assigned to you. Responsibilities: Support PhD students on their research methodologies, experimental designs, and theoretical approach. Participate actively in
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Basic qualifications (required at time of application) PhD or equivalent international degree, or enrolled in a PhD or equivalent international degree granting program Additional qualifications (required