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data storage capacity to accelerate research in intensive computing and large-scale data analytics, commonly referred to as Big Data. This characteristic distinguishes the HPC center at the university
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tools are need during the development of new imaging and sensing systems. With the rapid deployment of data-driven methods, repliable uncertainty quantification remains a big challenge that requires
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large EHRs, claims, or omics datasets. Ability to conduct research independently and within a team. Minimum Qualifications PhD in data science, biomedical informatics, computational biology, neuroscience
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analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems. The department has extensive
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cutting-edge big/deep data analysis methods, including machine learning and artificial intelligence. The ideal candidate will therefore have a strong background in data science and in the application and
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. Position Responsibilities Develop and implement machine learning and deep learning models to analyze and interpret high-throughput functional genomics data, such as ChIP-seq, RNA-seq, and ATAC-seq
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computational biology/chemistry, machine-learning for biological or chemical data, and drug discovery/design. Mentorship is taken seriously and every effort will be made to ensure the candidate is able to achieve
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computational biology/chemistry, machine-learning for biological or chemical data, and drug discovery/design. Mentorship is taken seriously and every effort will be made to ensure the candidate is able to achieve
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. Ready to be part of our team? Let’s shape the future together! About the team: The Computational Materials Discovery group is looking for a postdoctoral researcher working in the field of machine learning
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, Machine Learning and Autonomy Computational Biology / Data Analytics Appl Deadline: 2026/06/17 11:59PM (posted 2025/06/17) Position Description: Apply Position Description The Skinnider Lab at Princeton