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of Bayesian inference, data science, and machine learning. Experience in scientific computing in C++, Python, and/or Julia. Knowledge of electrochemistry and materials science (desirable). Ability to work as a
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modeling. At camp youth learn how to take care of themselves and become more independent while enjoying a variety of classes and socializing with peers, teen leaders and adults from differing backgrounds. 4
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interested in candidates with a strong technical background in medical image computing, machine learning, computer vision, or multimodal AI, together with excellent programming and communication skills
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of Marine Technology at NTNU has a vacancy for a PhD Candidate in Deep Learning enhanced FSI modelling of Multi-modular Floating Structures. The position is part of the AIMOS project (Artificial Intelligence
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Veterinärmedizinische Universität Wien (University of Veterinary Medicine Vienna) | Austria | about 2 months ago
Tyrol with state-of-the-art shotgun metagenomics, microbiome and resistome analyses, ecological network modelling, machine learning, and synthetic microbial community (SynCom) experiments. The PhD
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, ideally molecular dynamics and/or DFT. Scientific programming skills, particularly in Python, are expected. Familiarity with machine learning or generative AI methods applied to materials would be a strong
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Overview This is an exciting opportunity for a Research Associate from an engineering (systems, control or signal processing) or computer science (machine learning or data-driven modelling
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Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong
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community. To learn more about Keene State College, the University System of New Hampshire, and the Keene community, please visit: https://www.keene.edu , https://www.usnh.edu/ or https://www.ci.keene.nh.us
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machine learning-based model to map satellite retrievals to ground based air pollutant concentrations Conducting error assessment on the derived concentration data Implementing new observational data