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Field
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, an agent-based physiological 3D model of mitochondria based on the seminal work of Prof. Skupin (https://www.nature.com/articles/s41598-019-54159-1 ) will be extended and embedded into a continuous
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performance of molecular dynamics studies on molecular diffusion models in membranes. The research is oriented towards the study of the physicochemical behavior of new systems based on paramagnetic ions with
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, cargo, harbors etc. Large and deep AI models can be built using these data sets and machine learning, which can be combined with real-time satellite-based AIS data and sensors such as radar and algorithms
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and hydroeconomics. You have strong quantitative and methodological skills, such as (spatial) data analysis, hydrological modelling, AI-based or agent-based modelling. You have experience with
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evaluating temporal changes in A. longifolia populations and for assessing the impact of biological control agents. The work will comprise the application of remote-sensing and geospatial analysis
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-waiting – were successful spies in seventeenth-century England. This is what Nadine Akkerman describes in her book Invisible Agents, the first analysis of the role of female spies in the seventeenth century
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in the Cloud–Edge Continuum Non-Invasive and Semantic-based IoT stream processing framework with Agentic AI for Next-Generation Trustworthy Wearable and Ambient Systems Trust and Generative AI in Agile
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-motivation and interest to learn new skills Great to have: Experience programming in Python, Julia, or C/C++ Experience with Mathematica Experience with finite element methods, agent-based simulations, and/or
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designing, developing and evaluating systems and models to enhance learning through AI technology. The PhD fellow will engage with developing and evaluating models and agents, as well as, multi-agent networks
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agents. Your research will explore how reinforcement learning, multi-agent cooperation and generative worldmodels can deliver adaptive strategies that thrive amid volatile, multi-asset markets, micro