147 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Princeton University
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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data dissemination capabilities for making high-resolution earth system model output available to a diverse audience. Candidates must have a PhD in computer science, environmental and physical sciences
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members instruct approximately 5,200 undergraduate students and 2,600 graduate students. The University's generous financial aid program ensures that talented students from all economic backgrounds can
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commitment to undergraduate teaching.Today, more than 1,100 faculty members instruct approximately 5,200 undergraduate students and 2,600 graduate students. The University's generous financial aid program
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approach with a focus on cryo-EM. The postdoctoral scholar will have access to cutting-edge cryo-EM instrumentation and computational resources through the various core facilities at Princeton University
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of supplies, reagents and samples. Minimum Required Knowledge, Skills, Competencies, and Abilities *PhD required *Prior experience working in a research environment *Experience in working in a microbiology lab
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and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu). The position is for one year with the possibility of reappointment based on satisfactory performance and
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: 272265297 Position: Postdoctoral Research Associate Description: The Princeton Center for Statistics and Machine Learning (CSML) invites applications for DataX Postdoctoral Research Associate positions
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to undergraduate teaching.Today, more than 1,100 faculty members instruct approximately 5,200 undergraduate students and 2,600 graduate students. The University's generous financial aid program ensures that talented
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; record linkage/entity resolution; data privacy techniques; large data processing and high performance computing; advanced causal inference and statistics; computer vision and novel applications of machine