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techniques including graph neural networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty
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networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty quantification in forecasting and
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, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
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. reinforcement learning, Bayesian modelling, or other formal models of decision-making and learning). Furthermore, your suitability is further supported by: a track record of publications in peer-reviewed journals
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), and physiological parameters in the study of animal behaviour; a strong background in data analysis using R, preferably experience with Bayesian statistics and social network analysis; lab experience
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, Bayesian modelling, or other formal models of decision-making and learning). Furthermore, your suitability is further supported by: a track record of publications in peer-reviewed journals; the ability
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://pomplunlab.com/ ) at Leiden University. What you will do Project objectives Discover and develop high affinity peptide binders for therapeutically relevant receptors Establish robust peptide discovery workflows in
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surgical robots requiring precise manipulation to manufacturing systems handling delicate objects such as fruits. Working on this project positions you at the forefront of robotics in the burgeoning field
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://www.universiteitleiden.nl/en/staffmembers/laura-heitman#tab-1 at Leiden University! What you will do Project objectives are: Develop expression and purification methodologies for GPCRs Develop and optimize affinity selection
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objective is to investigate how new technologies challenge moral values and ontological concepts (like “nature”, “human being” and “community”), and how these challenges necessitate a revision of these