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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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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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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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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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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
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electrolysis. As an MSc student, you will design and implement a suite of AI “agents” (autoencoders, statistical models, LSTMs and LLM-based rule engines) that process historical and live sensor data (voltage
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and/or AI-based approaches), including the integration of concepts such as reinforcement learning and multi-agent reinforcement learning Further development and use of virtual test environments
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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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will be the agent-based model AgriPoliS (Agricultural Policy Simulator), developed at IAMO to investigate long-term structural change in agriculture under varying market, policy, and environmental
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or mechanical engineering well above average and you should have experience in agent-based modelling and simulation. Fundamental knowledge of the key disciplines in aerospace engineering and especially in airport