125 phd-studenship-in-computer-vision-and-machine-learning positions at Brookhaven Lab
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in real-world environments. Position Description: The CFN is seeking an exceptional researcher to pursue frontier research in artificial intelligence and machine learning (AI/ML) to advance scientific
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Apply Now Job ID JR101931Date posted 07/22/2025 As a Quality-of-Life Specialist, you will leverage advanced administrative expertise and program-management skills to lead high-profile QOL/BERA
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operation of particle or nuclear physics detectors Experience in applying machine learning to scientific problems Experience in distributed computing, workflow management and/or data management software
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or Adult BOCES certification, AA degree in a technical field, CCI program completion at BNL, or Technical high school certification program (eg. BOCES) and 1 year relevant work experience, or 2 years
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Knowledge, Skills, and Abilities: PhD in Chemistry, Physics, Biophysics, Biology, Biochemistry or Structural Biology. Proven ability to optimize peptide, protein or nucleic acid crystallization systems. Basic
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studies and computer simulations Collaborate with the BMAD development team at Cornell University by implementing new features into the code Participate in the EIC design effort in a more general sense
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work closely with CFN Electron Microscopy group members and computer scientists at Brookhaven. You will be professionally mentored by Dr. Judith Yang and Dr. Sooyeon Hwang and receive guidance from Prof
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analysis of atmospheric numerical model output (e.g., WRF, PALM, SAM) Experience with machine learning and artificial intelligence techniques Experience with predictive modeling Environmental, Health
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work experience with demonstrated expertise in electron polarization. Expertise in all requisite computer codes such as MAD, SLIM, BMAD. Excellent understanding of radiative polarization in general
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, enhanced by machine-learning and data-driven analysis techniques. Additionally, the study will encompass electrically triggered events that mimic the voltage-based signaling of biological synapses