142 high-performance-computing-"https:"-"CIPMM---Systemic-Neurophysiology" "https:" "https:" positions at Nature Careers
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are programmed. This includes defining novel programming methods and compiler infrastructures to deploy optimized software onto heterogeneous computing systems in both the embedded and high-performance computing
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280 international programs of different types. The University has currently established four world-classdisciplinary groups: computer data science, materials chemistry, biomedical science, and economics
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to collaborate closely with existing structures, such as the FAU Competence Center Engineering of Advanced Materials (FAU EAM), the Erlangen National High-Performance Computing Center (NHR@FAU), the FAU Data
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cytometry, non-invasive whole animal imaging, spatial transcriptomics and mass spectrometry, gnotobiotic research, advanced genomics, proteomics, and metabolomics, and high-performance computing. Candidate
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at the Professor, Associate Professor, and Assistant Professor levels. Post Specification The ideal candidates are expected to perform research and teaching in the following areas: Mathematics, Physics, Chemistry
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, or other relevant languages. Familiarity with frameworks for studying race, racism, and health disparities. Experience developing reproducible pipelines and working in high-performance computing environments
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recognized for high performance in numerous clinical specialties and procedures. Required Licensure/Certifications Candidates should hold an M.D. or M.D./Ph.D degree (or equivalent), have completed relevant
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cutting-edge research in areas such as pattern recognition, automation science, complex systems, AI for Science, robotics, machine learning, computer vision, natural language processing, biometrics, medical
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, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in
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and Models of Cognition. Cambridge University Press, 2014. Z. Huang et al. Towards high-performance spiking transformers from ann to snn conversion. ACM Transactions on Neural Networks and Learning