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and optimize specific functions. The ICMCB carries out fundamental research on model materials and/or materials with potential applications. The ICMCB is a UMR with an average of 280 agents (permanent
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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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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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to prostate cancer progression. This information will support the setting up of a clinical trial where anti-microbial agents are used to prevent prostate cancer development, resulting in significant
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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