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vision and novel applications of machine learning. Advanced knowledge of R or Python is required. Intermediate knowledge in C/C++ and/or at least one SQL dialect is preferred. Apply online at https
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techniques; large data processing and high performance computing; advanced causal inference and statistics; computer vision and novel applications of machine learning. Advanced knowledge of R or Python is
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postdoctoral or more senior researcher interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling
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engagement, enabling distinctive impact, both academic and societal. In this context, we welcome applicants from diverse backgrounds such as computer science, electrical and computer engineering, sociology
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: 280812534 2025 Postdoctoral Research Associate - AI/machine learning for analytical and forensic chemistry D-25-LPB-00006 | Research | Ludwig Princeton Branch The Skinnider Lab at Princeton University aims
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: 281113699 Postdoctoral Research Associate D-25-ECE-00003 | Research | Electrical and Computer Engineering The Department of Electrical and Computer Engineering invites applications for postdoctoral, or more
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applications for postdoctoral or more senior research associate positions broadly in statistics and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu). The position is for one year
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techniques; large data processing and high performance computing; advanced causal inference and statistics; computer vision and novel applications of machine learning. Advanced knowledge of R or Python is
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at the Lewis-Sigler Institute for Integrative Genomics and the Computer Science Department at Princeton University. We seek candidates with computational biology, bioinformatics, computer science, machine
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areas: -developmental biology -experimental and/or theoretical biophysics -experimental and/or computational genomics -computer science, statistics, and/or machine learning with applications relevant