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Field
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objectives. Troubleshoot analytical methods. Acquire, organize, and curate relevant datasets needed for statistical analyses. Oversee project activities, including potential supervision of student workers
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design, such as Bayesian Adaptive Clinical trial design or established expertise in statistical methods such as structural equation modeling, causal data analysis. Experience in serving in protocol review
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to facilitate the accomplishment of biodiversity conservation research objectives. Develops and writes new proposals to secure contracts for grant-funded research related to biodiversity conservation and the use
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-GRASP”, “Simulation-Based Bayesian Inference for Object Perception in Robot Grasping”, financed by the European Union´s Horizon Europe research & innovation programme under the euROBIN project (Grant
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) for engineering systems and structures, as well as expertise in machine learning, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R
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, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R. Teamwork and Responsibility: Ability to work effectively within a project team
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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key role in delivering the following objectives: Develop and validate advanced cardiovascular risk prediction models, including multi-outcome and dynamic models tailored to complex, multimorbid
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meet the goals and objectives of the department and institution. Minimum Education and/or Training: Bachelor's degree in mathematics, engineering, or computer science with advanced training in
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, and analysis of large, diverse datasets that benefit from high-performance computing (HPC) clusters. The objective of these fellowships is to facilitate cross-disciplinary, cross-location research