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Field
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will be to: identify policy mechanisms that can change infrastructure affordance and lower infrastructure use develop an agent-based model that regards infrastructure as a socio-technical system reveal
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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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health, and bioinformatics. You will apply advanced AI methods - from classical machine learning to large language models and agent-based AI - on large-scale healthcare datasets, including structured
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(mammalian cell cultures) and whole tissue level (preclinical cancer models). Main Tasks and responsibilities: The PhD candidate researcher will focus primarily on laboratory-based experimental work in the
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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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model-based control tailored to systems like NudgeFlow. The framework decomposes the global control problem into smaller local problems for each zone or room, enabling independent optimization with local
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of the anti-MMP9 Nano-antibodies in a mouse model of delayed healing and a 3 weeks training at Nordic Bioscience( Herlev, Denmark) in the team of Dr. Signe Holm Nielsen to establish a Nano-antibodies based
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children with autism and eating behavior challenges. This research explores how large language models (LLMs), interactive robotics, and multimodal technologies can support creative expression and
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for Additive Manufacturing of the Biomaterials and Tissues of the Future. You will be one of 17 doctoral students across the entire network, and one of three doctoral candidates based in this particular research
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combine engineering design principles with stake-holder engagement tools to support decision-making under uncertainty. Using methods like agent-based modelling, quantitative resilience and risk analysis