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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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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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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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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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. Research Focus The rise of powerful Large Language Models (LLMs) and autonomous AI agents is set to fundamentally change the practice of software engineering. As AI evolves from simple co-pilots to agents
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research and teaching in Service Engineering. Her research aims to build models for understanding the impact of customer and agent behavior on service systems and to incorporate these behaviors
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and verifiably safe. We consider resilience and intelligence to be part of the same process. What you will do Advancements in agent-based AI approaches are gaining significant attention in industry due
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, Python and/or Stata skills; Optional: Interest and experience in formal modelling (game theory, agent-based modelling); Competent handling of MS Office applications. Our offer Remuneration in accordance
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ideas and immerse yourself in innovative research fields, including: Traffic optimization using multi-agent Reinforcement Learning and hierarchical Reinforcement Learning Reinforcement Learning-based