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modelling of cell–cell interactions, cell-state transitions, and tissue dynamics and multi-omics integration Applying ML approaches for biomarker discovery, predictive modelling, and development of diagnostic
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to simulate full system performance. You will work closely with the project partners to determine design specifications and also after the prototype has been realized to compare model prediction with actual
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considered an advantage if you have: Experience with protein language models (e.g., ESM, ProtT5) Experience with structure prediction frameworks Experience in geometric deep learning or graph neural networks
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modelling of cell–cell interactions, cell-state transitions, and tissue dynamics and multi-omics integration Applying ML approaches for biomarker discovery, predictive modelling, and development of diagnostic
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on Nanoparticles You will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network
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building materials using polyphasic detection and identification approaches. Characterization of biobased building materials with respect to their moisture sorption isotherms. Modelling the correlation
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, Recombinant protein expression and purification, biochemical and biophysical characterization of nucleic acids Computational model building and structure prediction Single-molecule fluorescence microscopy
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materials, (d) Artificial Intelligence (AI) models to predict and control the construction process, (e) a digital twin / information backbone that enables cohesive operation of the design and production
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, aimed at uncovering the key traits that define successful microbial biofertilizers, and to develop predictive models that can guide the rational design of next-generation BioAg products tailored
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tools or functional genomic information or OMICS to improve genomic prediction models. The persons hired will collaborate with industry partners, teach at undergraduate and graduate levels, and supervise