67 parallel-computing-numerical-methods-"Prof" positions at Chalmers University of Technology in Sweden
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fundamental questions about the particles and forces governing our Universe to energy-related research. The methods of our investigations are also diverse and complementary, and range from theory and computer
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actions to evaluate, balancing safety and computational effort. You will compare deep learning–based methods and probabilistic machine learning approaches, and explore extensions to active reachability
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applying methods from quantum field theory, computational physics, statistics, and applied mathematics. Within astroparticle physics, our focus spans from the theoretical modeling of systems and phenomena
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theoretical research, algorithm design, and the development of software tools that demonstrate the applicability of the new methods. Research environment The positions are hosted by the Department
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expertise in a range of quantitative social science methods applied both to "softer" kinds of data like texts of policies and political documents as well as to numerical data such as technology deployment
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communication with the Swedish marine industry will take place on a regular basis. Main responsibilities As a full-time doctoral student, you will be expected to independently develop numerical methods and codes
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to reach the application form. Interview: In case your application passes the first round, you will be invited for an interview with Prof. Timur Shegai. This interview will be based on your previous research
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(AIMLeNS) lab is a tight-knit team of computer scientists, chemists, physicists, and mathematicians working collaboratively. Our focus is on developing practical methods that blend traditional disciplines
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methods for creating innovative heat-exchanger geometries with high efficiency and sustainability in mind. You will use open-source machine learning software provided by Meta AI and OpenAI, deployed
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changing environment will affect the stability of quick clays, and the probability of triggering catastrophic failures. We offer access to unique experimental facilities and computational tools developed by