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will use machine learning methods to develop affinity ligands. These methods have been transformative for protein design, allowing generation of novel proteins which can suit a precise need. In this 4
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control
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and documented background in machine learning, deep learning, data analysis and programming. Previous experience in research and knowledge in bioinformatics, biophysics, biochemistry, molecular biology
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the Division for Computer network and systems and the employment is placed with Chalmers University of Technology. Our research spans from theoretical computer science to applied systems development. We provide
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approach that integrates wireless communication, computer vision, and machine learning to optimize PC transmission from sensors to an edge server for remote registration. The research is funded by Wallenberg
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of robotics, electromobility and autonomous driving. We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and
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totaling 60 ECTS credits) and join an international research team with backgrounds in sociology, political science, network science, statistics, and machine learning. More information on the PhD program can
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/thesis: Challenges and opportunities with remote sensing and machine learning in forestry Research subject : Soil science Description: WIFORCE Research School Do you want to contribute to the future
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consist of the following: Mathematical analysis of ecological and eco-evolutionary models, involving pencil-and-paper calculations; Computer simulations of more complex models which do not easily lend
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. The position is placed in the Division for Computer Networks and Systems and is formally employed by Chalmers University of Technology. Our research spans from theoretical computer science to applied systems