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, but their long-term stability remains a key challenge for large-scale deployment. In this PhD project, you will use density functional theory and state-of-the-art machine-learning interatomic potentials
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materials science, and machine-learning-driven materials discovery. The successful candidate is expected to have experience in areas such as high-throughput DFT calculations, thermodynamic and phase
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analytically underexplored conditions. It relies on modern machine learning approaches to model fragmentation behavior, support spectral interpretation, and enable future advances in peptide identification
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in Work Package 2, you will: Design, manufacture, and characterize novel composites Perform vibro-acoustic testing and DMA-based damping analysis Develop FEM/BEM and machine-learning models to
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, and open scientific resources where relevant Required Qualifications Applicants must have: A PhD in computational proteomics, bioinformatics, machine learning, computer science, chemometrics
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) two funded 3-month secondments in the industry in TDC NET (Denmark) and Dell Technologies (Ireland); and a network-wide training program. Responsibilities and qualifications The PhD scholarship is on
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Job Description If you are looking to establish yourself as a researcher in the cross-disciplinary fields of machine learning and mental health, this postdoc position is for you. At DTU Compute you
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Job Description Are you interested in using automation and machine learning to make the discovery of new materials and catalysts faster and more sustainable? Do you want to start your scientific
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part of a world leading research environment and contribute to the development for the next generation machine learning tools for power systems. One Postdoc will be involved in the newly funded project
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Postdoc in AI-driven design of environment-responsive antibodies and nanobodies - DTU Bioengineering
students We are looking for a candidate who: Holds a PhD in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field Has strong theoretical and