22 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Brookhaven Lab
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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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, and Abilities: Experience in NGS library preparation and data analyses. Bioinformatic/programming skills (MatLab, Python, R, etc). Experience in application of Artificial Intelligence/Machine Learning
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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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detection equipment to support sponsors in operation and research. In addition, the Group also works on artificial intelligence and machine learning to address questions related to detection and safeguards
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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
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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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for the characterization. Prepare research based manuscripts for publication Participate in the meetings related to the EFRC Required Knowledge, Skills, and Abilities: PhD in Condensed Matter Physics, Materials Science
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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
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, adhering to a Design-Build-Test-Learn methodology for integrated research. Within the Schwender group, the primary objective is to assess the outcomes of metabolic engineering endeavors aimed at enhancing