956 machine-learning-"https:"-"https:"-"https:"-"https:"-"Ulster-University" Fellowship positions
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maritime transport, marine technology, computer science, or a related field; Excellent programming skills, such as Python, Matlab, C++, or other computer languages; A record of publications in reputable peer
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: Masters/Ph.D. degree in Electrical/Computer Engineering, specialized in Power Systems/Renewable Energy Planning/Optimization Expertise in medium and low voltage network modelling, power-flow, and large
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. In addition, you must have: a solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning (ML), and data-driven modeling documented experience
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. market access. The approach will include metagenomics and bioinformatics to understand genetic diversity of the pathogen. Learning Objectives: During this project, the participant will be involved in
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instruments Ability to occasionally lift and move laboratory supplies or equipment weighing up to approximately 25 pounds Ability to work at a computer workstation for data analysis and manuscript preparation
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successful candidate, you will: This project with Dr. Nagarajan Vaidehi involves developing and application of interpretable machine learning methods to uncover allosteric regulation of disordered regions in
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presentations at regional and national conferences will be supported. Learning Objectives: Under the guidance of a mentor, specific learning objectives include: Conducting independent research aimed at developing
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will be advantageous. Knowledge of machine learning or reinforcement learning techniques will be advantageous. Proficiency in algorithm development using Python will be advantageous
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, such as, geometric/topological/algebraic data analysis, geometric/topological deep learning, Math for AI, categorical deep learning, sheaf neural networks, PINN/KAN models, neural operators, etc, and
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include: Item response theory, causal inference in non-experimental designs, psychometrics, randomized controlled field trials, longitudinal and multilevel modelling, machine learning methods, artificial