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: Development of ML-based tools for analysis and engineering protein dynamics PhD enrolment: Czech Technical University in Prague DC14: Machine learning for Empirical Valence Bond (EVB) simulations to engineer
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the DFG Priority Programme “Molecular Machine Learning” and embedded in the research project “Multi-fidelity, active learning strategies for exciton transfer in cryptophyte antenna complexes”. The PhD
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modelling predictions. Experience or a strong interest in scientific programming and machine-learning-assisted data analysis for materials modelling is an advantage. PhD Position 2 – Coarse-Grained and
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | 1 day ago
include creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search
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software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics or related Experience in either machine learning or computational biology. Interest in both
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Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 36-month PhD studentship will contribute to cutting-edge advancements in automated drug discovery through
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Engineering and Mechatronics. The Proposed PhD thesis topic: “Intelligent Diagnostics of Electrical Machines through AI-Enabled IoT Systems: Design of Custom Embedded Hardware and Protocol-Aware Architectures
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University, Tyndall National Institute, and Seagate Technology, one of the global leaders in data storage and photonics innovation. Four PhD students will be based at ATU Donegal and will join the
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UNIVERSIDAD CATÓLICA DE MURCIA - FUNDACIÓN UNIVERSITARIA SAN ANTONIO DE MURCIA | Spain | 12 days ago
Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you passionate about Artificial Intelligence, Machine Learning
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data