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skills, including generalized linear models, multiple machine‑learning algorithms, MOFA and multi‑omics pathway analysis. · Strong background in experimental design, quantitative data analysis, and
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Job Type Staff Job Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived
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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the
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dynamics simulations and machine learning methods to study the structure and electrochemistry of disordered materials are also encouraged to apply. The project primarily aims to understand the complex
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experimental systems, from cell lines to primary neurons to ex vivo brain tissue. The postdoc will be encouraged to develop a unique scientific identity within the broader project, lead publications, present
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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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of sensorimotor processing. We are recruiting two postdocs: Postdoc in Computer Vision & AI for Behavior Analysis Postdoc in Embodied AI (Reinforcement Learning for Motor Control) Main duties and responsibilities
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machine learning within the scope of ACAG. The postdoc will have various opportunities for professional development, including contributing to and leading grant proposal development (e.g., external
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the computational task: first-principles active-site descriptor models, high-throughput screening with machine-learned interatomic potentials, and a dedicated catalysis database. This position is ideal for a
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The Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory (Berkeley Lab) is seeking a Postdoctoral Researcher – Scientific Machine Learning & Computational