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the state, Texas A&M AgriLife is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about joining AgriLife
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interdisciplinary research hub dedicated to the study of how machine learning and AI algorithms are transforming societies. LCAL pursues one of the most urgent questions for contemporary society: how do we wish
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educational opportunities, networking and presenting at local, national, and international meetings, and reading current primary literature. Provides a friendly working environment and be available to teach
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an outstanding environment in which to develop machine learning tools and engage with an interdisciplinary community of researchers with an interest in AI for healthcare. The post holder will have opportunities
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of the future Center and Protect OHIO, translating research findings into outreach-appropriate sessions, building active-learning sessions for stakeholders, learning best practices for educating and engaging
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of Position Temporary Position Time Status Full-Time Required Education PhD or PsyD Required Related Experience Completion of APA-Accredited Internship Required License/Registration/Certification None Physical
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analyse electrochemical free-energy landscapes and connect elementary-step energetics to catalytic performance apply machine-learning interatomic potentials and microkinetic models where appropriate
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You'll Bring Required Qualifications PhD or equivalent doctoral degree (ScD, MD, DVM, or related terminal degree). Strong background in one or more of the following: Computer Vision Machine Learning
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, Atmospheric Science, LSTMs, VAEs, PyTorch, TensorFlow, NumPy, pandas, scikit-learn, spatio-temporal datasets Additional Information Eligibility criteria Mandatory: *A PhD in Data Science, Artificial
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seizures/epilepsy, including stereotactic injections and implantation of multichannel recording and stimulation electrodes. Acquire, curate, and analyze multichannel iEEG/electrophysiology recordings from