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acids, ligands), coarse-grain and polymer model development, multiscale modeling, molecular simulation code/software (e.g., LAMMPS, GROMACS), machine learning. Prior experience with applying simulations
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will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
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to learn more about what the Bangor region has to offer. Qualifications: Required: Doctoral degree in engineering, environmental science, geoscience, or related field. Knowledge and experience in engineering
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skills and experience and interest in data analysis, data science, machine learning and process automation would be an advantage. Previous experience with XAS or other synchrotron-based techniques would be
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on the theoretical foundations of (Machine) Learning. These positions offer the opportunity for postdoctoral research in an intellectually vibrant, inclusive, and welcoming department. Selected candidates
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic
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regular basis with the experimental groups in the initiative. The position requires sound verbal and written communication skills in English. Swedish is not a requirement, but willingness to learn is
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, proteomics, metabolomics, microbiome). Strong expertise in machine learning, deep learning, and advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases
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CBS - Postdoctoral Position: Artificial Intelligence Applied to Metabolomics for Health Applications
, and Precision Health. The project aims to leverage AI and machine learning (ML) to analyze complex metabolomics datasets and address key health challenges, including biomarker discovery, disease