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Field
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of the following methodologies: optogenetics, calcium imaging, viral tracing, tissue clearing, murine behavioral phenotyping, machine-learning behavioral analysis Familiarity with programming languages
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dependable large-scale software systems, integrating expertise in: Software Engineering Machine Learning & MLOps Robotics & Cyber-Physical Systems Cloud & HPC ecosystems Interdisciplinary research. As a
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and the terms of the research grant when extending an offer. Completion of PhD in Data Science, Computer Science, Mathematics, or other discipline aligned with CDS faculty research by the start date
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL
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clustering, redshift-space distortions, weak/strong gravitational lensing, and artificial intelligence/machine learning (AI/ML). The observational focus is on optical sky surveys (DES, DESI, Roman, Rubin Obs
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training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills and familiarity with machine learning
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, Bash). Experience working in a Unix/Linux environment, including setting up and managing High Performance Computing (HPC) clusters. Familiarity with metagenomic data analysis and machine learning
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terrestrial networks, non-terrestrial network entanglement distribution. Your profile PhD degree in wireless communications, signal processing, machine/deep learning or a closely related field in Electrical and
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teaching and student supervision activities in areas related to your expertise. What we ask of you Your experience and profile A PhD degree in AI (e.g., machine learning, natural language processing
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data