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), mathematical evolutionary modeling (game theory, dynamical systems, agent-based simulations or other), bespoke probabilistic modeling / (Bayesian) data analysis (e.g., in the Rational Speech Act framework
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Project The PhD project DC7 aims to develop and apply a coupled Agent-Based Model (ABM) and couple it to the Regional Flood Model (RFM) to evaluate the effect of adaptive behaviour of small and medium-sized
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in the wild. The planned work involves optimizing IRL using agent-based simulations, applying IRL to empirical fish movement data to study reward functions in the context of adaptive strategies, and
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immune cells in cell culture and murine models of inflammation and cancer Collecting, analyzing and annotating MRI data Combining MRI and mass spectrometric imaging data in data base for quantitative MRI
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the Q methodology dataset build an agent-based modelling (ABM) in python or Netlogo visualize and interpret results; prepare a short report or presentation at the end Your qualifications: background in
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university degree (diploma, master's degree) in transport or traffic flow or related study programs. Solid knowledge of at least one programming language, preferably Python, experience in model-based
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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
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relevance. Your Tasks Antibody–Drug Conjugates (ADCs) are a rapidly advancing class of cancer therapeutics that combine the specificity of monoclonal antibodies with the potency of cytotoxic agents. Upon
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language processing (NLP), and fine-tuning techniques Familiarity with structured reasoning, chain-of-thought processes, and agent-based systems is beneficial Strong programming skills (preferably Python); experience
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vision models Experience with event-based cameras, neuromorphic vision concepts, spiking neural networks, and/or neuromorphic computing is a plus Experience with, or willingness to learn, ROS 2 for robotic