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Field
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Bayes factor hypothesis tests in factorial designs. What are you going to do The envisioned projects will focus on the following activities related to Bayesian inference in factorial designs: Construction
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on the following activities related to Bayesian inference in factorial designs: Construction and elicitation of informed prior distributions; Critical assessment of default prior distributions; Organizing a many
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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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, rental platforms, and production systems—where decision-making must balance conflicting objectives, leverage real-time data, and ultimately support sustainable profitability. Examples include optimizing
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In this role, you will be responsible of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with
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learning Deep learning model generalisation techniques Translating deep learning models into clinical settings Experience developing deep learning models for real-time image/video segmentation, object
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on the willingness to fight. This postdoc position is part of the project “What are Citizens Prepared to Fight for? The Netherlands in Comparative Perspective”. The overall objective of the project is to gain a deeper
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learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In this role, you will
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of developing cutting-edge deep learning models for real-time image and video analysis (e.g., segmentation, object tracking, reinforcement learning), with applications to medical imaging and robotic systems. In