21 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Brookhaven Lab in United States
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). Manuscript preparation and presentation of results at national and international meetings. Required Knowledge, Skills, and Abilities: PhD in Chemistry, or a related field. Preferred Knowledge, Skills, and
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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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, 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
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Apply Now Job ID JR101408Date posted 09/13/2024 The AI and Machine Learning Department at Brookhaven National Laboratory (BNL) invites exceptional candidates to apply for a post-doctoral research
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and relevant data analysis. • Demonstrated experience in Python programming. • Knowledge of machine-learning algorithms. Additional Information: BNL policy requires that after obtaining a PhD
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-based machine learning and natural language processing. Strong research experience (e.g., evidenced by publication record). Excellent programming and computer science skills. Security clearance
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applies platforms for state-of-the-art techniques for Accelerated Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a