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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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into reliable information about structural and aerodynamic behaviour remains a challenge. The PhD will develop data-driven methods that combine measurements, physics-based models, and machine learning to extract
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for a full-time, on-site PhD position in machine learning, forecasting and time series analysis. Reykjavik University, Department of Engineering. Duration: 3 years. Start date: Negotiable. Reykjavik
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the leadership of Principal Investigator Dr Andrew Siemion. Listen's interdisciplinary research has synergies with many of the department's research priorities, including exoplanet studies, machine learning
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diffraction data where the information extends towards 3-d space. Machine learning offers promising approaches for the solution of complex problems of disorder, ultimately aiming at general and automated
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programs to eligible team members. Learn more at: https://hr.duke.edu/benefits/ Equal Opportunity Employer: Duke is an Equal Opportunity Employer committed to providing employment opportunity without
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dissemination, and translational opportunities Job Requirements: PhD in Chemistry with a focus on computational/peptide/organic/machine learning or a closely related discipline At least one first-author
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of Computer and Information Science , within Linköping University . Your work assignments As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your
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comprehensive platform for data extraction, analysis, and version control, providing access to highly curated datasets in a machine learning-friendly format. This PhD is part of the CARES project (Chemically
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Your Job: As part of an interdisciplinary project team with researchers from bioinformatics you will work on quantum algorithms for drug discovery. Here, the focus lies on machine learning and