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patterns of genomic sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr. Eran Elhaik to design machine-learning models
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into elementary particle physics from model building and Dark Matter to formal Quantum Field Theory. Organizationally we are part of the division of Subatomic, High-Energy and Plasma Physics within the Department
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will be expected to dedicate your time develop a high-biofidelity, high-resolution computational rat model with dual applications: i) advance the mechanistic understanding of brain injury by linking
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ocean environments, ensure safe and sustainable operations. Our activities are centered on numerical modelling (e.g. CFD, FEA, FSI, optimization, machine learning), but also include experiments and real
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approach combines behavioral experiments, psychophysiology, computational modeling, and brain imaging (fMRI). We offer a dynamic, international research environment where you can contribute to top-level
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develop and improve protein-glycan binding prediction models and use AI, data science, and bioinformatics to identify and design glycan-binding proteins with desired binding specificities. Qualifications
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Project description The postdoctoral fellow will explore synaptic processes and white matter pathways between remote brain areas in vivo in animal models that underlie plasticity in the prefrontal
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. The workplace The position is located at the Laboratory of Organic Electronics (LOE ), specifically within the Theory and Modelling for Organic Electronics unit in the group led by Associate Professor Glib
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techniques • Explainable AI/ML using visualization • AI/ML-empowered visual analytics of multivariate networks (network embeddings, …) • Large Language Model (LLM)-assisted visual analytics of text, images
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compartments including single cell- or bulk sorted immune cells and extracellular vesicles from the lung of the patient cohort, as well as from cell culture model systems. The studies are performed in close