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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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– Multiscale imaging of organ-specific inflammation” and aims to develop and apply a quantitative MRI approach, by establishing a data base of simulated and experimental MR data of tissue and single cells
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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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agent, e.g., investigate LLM explainability evaluation metrics and conduct experiments. Provide support in the LLM agent project based on competencies. Collaborate with the team, LLM and health experts
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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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Your Job: This thesis focuses on designing, evaluating, and deploying algorithms for robot perception and control. The main task is predicting both self-motion and the motion of surrounding agents
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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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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 Leibniz Institute of Agricultural Development in Transition Economies (IAMO) in Halle (Saale), Germany, invites applications for a 1 Doctoral Scholarship on “Agent-based Analyses of Structural
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papers. You will be involved in several projects in cooperation with the departmental project groups “phytoeffectors”, “cancer agents & targeting”, “neuroactives”, and “peptides and mimetics”, support