1,105 machine-learning-"https:"-"https:"-"https:"-"Linnaeus-University" Fellowship positions
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transfer, thermo-fluid systems simulation and programming, as well as exposure to software platforms and algorithms for Artificial Intelligence, Machine Learning and Data Analytics. You will have experience
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(specifically PCECs). Proven experience in developing and validating numerical models (e.g., using COMSOL). Hands-on experience with programming for numerical optimization, machine learning, and data processing
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such as: Causal inference and the design and analysis of experiments Reinforcement learning and sequential decision-making Analysis of complex systems, networks, and large-scale data Machine learning
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microbiome studies, microfluidics, or/and Machine-Learning to join our dynamic research team. The successful candidate will play a pivotal role in advancing our understanding of microbial communities through
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of Alabama and beyond. The successful candidate will apply tools including (but not limited to) Data Acquisitions, Data Mining, Data Visualization, Machine Learning, Statistics, Optimization and Simulation in
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) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena. Experts in this new discipline of
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information/data from one structure can be used to manage similar structures from the same population. New machine learning, sensing and digital twin technologies will be developed with the aim of driving new
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, building automation and control (BAC) system, Artificial Intelligence (AI) & Machine-Learning (ML) applications. Good written and oral communication skills Proficiency in power system modelling, advanced
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The postdoctoral fellow will lead and co-lead projects that combine computational modeling, machine learning, and EEG to answer questions about scene understanding and neural representation. The fellow will work
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application of innovative Machine Learning (ML) frameworks to understand and predict the global hydrological cycle. The role will require bridging the gap between process-based physical modeling and scalable