80 machine-learning-"https:" "https:" "https:" "https:" "https:" Postdoctoral research jobs in Sweden
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and refined our pioneering AI-driven methods. This project focuses on improving protein structure prediction, design, quality assessment, and dynamics using innovative machine learning techniques. You
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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technology. We are located on LTH's campus in northern Lund. At the Division of Electromagnetics and Nanoelectronics within the Department, we develop and study new generations of electronics based on advanced
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mathematics, data science and machine learning for image recognition. Moreover, you will develop methods and software that will allow new characterization of nanoscale materials. Therefore, your research will
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experience with machine learning techniques in general and neural networks in particular will be highly beneficial. About the employment The employment is a temporary position of 2 years according to central
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the Division of Data Science and Artificial Intelligence and the employment is with Chalmers University of Technology. The division’s research spans from foundational machine learning theory to applications
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academic research, learning and outreach. We provide a competitive advantage by linking our top-level international and interdisciplinary academic performance in the areas of material science, nanotechnology
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measurement and control platform for optimal island operation of Chalmers’ wind-battery system. Machine learning-based forecasting tools for renewable production using limited local measurement data
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, Automotive, and Mechanical Engineering, and provides professional education nationally and internationally, supporting lifelong learning. M2 strives for close collaboration between academia, industry, and
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: S. Aalto). In the project we use multi-wavelength techniques, including recently developed mm and submm observational methods, to reach into the dark hearts of dusty galaxies. New machine learning