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. The goal is to build foundation models capable of learning from richly structured or semi-structured data where traditional graph neural networks may fall short, enabling better representation, inference
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on this website: https://www.kmk.org/zab/central-office-for-foreign-education.html For further information or to discuss the position, please contact Dr. Jessica Strefler .
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regiona economic players. With a presence in the fields of computational neuroscience and biology, data science and modeling
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-derived organoid models. You will work closely with in-house technology platforms, including the Single Cell Genomics Facility, Big Data Core and High Throughput Screening Facility. Our research is embedded
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available single-cell sequencing data generated from patient samples and mouse models, we will enhance and apply machine-learning based algorithms to deconvolute bulk tumor RNA-seq samples to distinct immune
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injection, ion transfer, and structural dynamics in realistic and model systems for battery materials. The position will span experimental efforts at large scale X-ray facilities, handling and reduction
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to interact with data engineers and IT specialists for model integration. Experience in communicating results through project reports, technical notes or publications is considered a plus. Language skills
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(TMJ) degeneration and pain, and salivary gland degeneration. The lab uses mouse and human iPSC/organoid models equipped with genomic, single cell RNA sequencing, spatial transcriptomics, tissue clearing
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limited understanding of how tumors systemically impair immune responses. Meanwhile, traditional animal models often fail to capture the complexity of human immune-cancer interactions. This position