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transitions and universality for spectral statistics of random matrices and their applications in high-dimensional statistics, machine learning and probability theory. The Department of Mathematics at KTH
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the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
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deadline Experience with urban acoustic monitoring or transportation noise assessment Programming skills in Python Knowledge of machine learning techniques applied to acoustic or environmental data
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for machine learning, e.g. PyTorch or TensorFlow. Strong ability in spoken and written Swedish. Assessment of the applicants will primarily be based on scientific merits and potential as researchers. Special
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geometries. However, AM-generated surfaces exhibit significant and highly irregular roughness, a key factor that strongly modifies turbulence, pressure drop, and heat transfer. Unlike conventional machined
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, development of chemical process solutions for repurposing of electrodes, and integration of AI-based vision and active machine learning to optimize the efficiency of the process. Writing publications and
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factor that strongly modifies turbulence, pressure drop, and heat transfer. Unlike conventional machined roughness, AM roughness is characterized by randomness, porosity, and powder adhesion, producing
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and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. About the research project The postdoc will work at Chalmers University of Technology in a
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Experience in machine learning Knowledge of SDN and NFV Knowledge of basic TCP/IP protocols What you will do Conduct high-impact research and publish in leading journals and conferences Shape research
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systems. Combination of behavior with large-scale neural recordings using silicon probes, miniscope, or 2P imaging. Ability to explore and analyze large datasets using modern machine learning methods and a