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Artificial Intelligence (AI) and Machine Learning (ML). In this position students will contribute to research projects in CKL and as part of their education, will also engage in a dedicated 6-months internship
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workflows, including adaptive, automated, or agent-based (agentic) workflows that integrate simulation, data analysis, and/or machine learning. Experience with computational workflows on large-scale HPC
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, cell biology, time-lapse microscopy and single cell analysis. You will work in a dynamic and highly interdisciplinary team including computer scientists, experimentalists and clinicians. You will be
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astrophysics (completed by the start date), demonstrated experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in
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of the following topics: data collection, information extraction using large language models (LLMs), machine learning (ML), natural language processing (NLP) Expertise in software development with
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· Introduction to Biomedical Informatics · Leadership and Innovation for Informatics · Concepts in Computer Programming · Ethics and Policy Questions: Genomics, Healthcare and Big Data
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at: www.fz-juelich.de/gp/Careers_Docs Further information on doctoral degrees at Forschungszentrum Jülich (including its various branch offices) is available at https://www.fz-juelich.de/en/careers/phd We
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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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disorder. This project investigates early neural markers of psychosis by integrating multimodal neuroimaging with genetic and transcriptomic data and applying machine-learning approaches to identify
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and Data-Driven Discovery, which involves creating a large, unique dataset linking composition to phase stability and fundamental mechanical properties for data-driven down-selection. The second pillar