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broad research and education in AI both from a technical and interdisciplinary perspective. Coordinator of TrustLLM a large EU-project developing trustworthy and factual language models. Host
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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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. 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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of policy-driven technologies. The position will focus on developing new methods to measure and model technological deployment and to identify the role of policies in accelerating and/or re-accelerating
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groundbreaking work on enzyme-triggered electrode fabrication in animal models (Strakosas et al., Science, 2023; https://doi.org/10.1126/science.adc9998 ), this project leverages enzymatic polymerization to create
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for self-deployable 6G networks in the edge continuum (EC-DEPLOY-6G) pioneers the use of large language model–driven agents to autonomously configure and deploy 6G and cloud functions. The project leverages
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models, applying CRISPR/Cas9 based gene knockout or other base/prime editing methods for human cells, such as T and B cells, induced pluripotent stem cells (iPSCs) and hematopoietic stem cells, and
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molecular and cell biology assays, generating iPSC and organoid models, applying CRISPR/Cas9 based gene knockout or other base/prime editing methods for human cells, such as T and B cells, induced pluripotent
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written communication skills in English. Contract terms This is a full-time postdoctoral position with a fixed term of two years. Supervision and support will be provided for both experimental and modeling