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techniques (e.g. explainable, ethical, empathic and agentic AI), natural language processing (NLP), large language models (LLMs), data science methods, and mHealth to analyze large-scale, multidimensional, and
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time. Collaborate on project and analysis design guided by their PI. Develop new computational methods. Adhere to field and lab standards for data analysis. Identify, process, organize, interpret, review
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analyzing data from large cohorts, ideally within the NAKO German National Cohort Experience in teaching epidemiology to students Desirable qualifications Experience in the field of metabolic diseases
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, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific literature). Our
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SAXS and Large scale facilities SAXS (from proposal writing to execution of experiments and data analysis for the latter) Experience with protein purification Advanced knowledge in research design and
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data mining. The group provides a strong network to local AI expertise (e. g. Hessian.AI, TU Darmstadt), large scale compute infrastructure, as well as a broad international network (Stanford, UC San
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about 300,000 inhabitants and the second largest city in Denmark. Aarhus is large enough to have a rich cultural scene and international community, but small enough to not feel crowded. It is a safe and
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given to applicants who have received their Ph.D. degrees in behavioral neuroscience or related fields within the last 3 years and have experience in computational neuroscience and data mining using
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the integration of large-scale biological datasets derived from both the host and the microbiome, employing advanced statistical methods and cutting-edge artificial intelligence techniques to uncover novel insights
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collaborative research on immune development and responses to respiratory viruses. Analyze clinical and large-scale datasets (e.g., genomic, transcriptomic, proteomic data). Design and execute experiments using