35 mathematical-analysis-math-physics PhD positions at Chalmers University of Technology
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Analysis . We conduct research to find more sustainable technology solutions and ways to transform technological systems to meet the environmental and resource constraints faced by society. Our work is
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qualifications Documented experience with data analysis and programming (e.g., Matlab, Python or R). Experience of risk assessment and/or decision analysis Experience of probabilistic methods such as Monte Carlo
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240 higher education credits in Applied Mathematics, Applied Physics, Electrical Engineering, Mechanical Engineering, or a related field. A strong mathematical foundation and excellent academic
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qualifications: To be eligible for this position, you must have (or be close to completing) a Master’s degree corresponding to at least 240 higher education credits in Applied Mathematics, Applied Physics
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and development Generative AI models for sound, music, visuals, 3D graphics, or movement Projects related to Generative AI Background in mathematics and statistics of Deep Learning What you will do
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(masterexamen) of 120 credits or a Master’s degree (magisterexamen) of 60 credits in Electrical Engineering, Communication Engineering, Engineering Physics, Computer Engineering or similar, with a strong
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mathematical foundations in theoretical computer science. Good verbal and written communication skills in English. Swedish is not required for this position. Chalmers offers free language courses for those
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qualifications Marine biogeochemical processes Hydrodynamic processes related to ships, turbulence, or mixing Oceanographic modelling Data analysis and programming (e.g., MATLAB, Python, or R) Interdisciplinary
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benefit analysis with more practical aspects, such as implementation and real-world applications, in collaboration with project partners Volvo Cars and Autoliv. About us The project is located within
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the training process to several network threats, such as DDoS attacks, traffic hijacking, and traffic analysis. While these risks are well-studied in existing literature, their impacts on distributed AI training