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Field
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technologies, ethical implications, and governance frameworks, including knowledge of algorithmic accountability and transparency. Experience with both qualitative and quantitative research methods, and
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power engineering. In condition monitoring non-invasive data is analyzed through machine learning algorithms or by statistical methods. The aim of predictive analysis is to use non-invasive methods
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and dynamic models. Development of optimization algorithms for the unique operational characteristics of the onshore microgrids. Development energy management system including frontend and backend. Job
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Experience: Knowledge of AI frameworks and algorithms, particularly those related to decision-making and ethical AI. Machine Learning Experience: Knowledge of ML techniques, including reinforcement learning
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electronic systems with a focus on inverters and active filter-based harmonic mitigation, thermal performance, and control strategies. Develop and implement advanced control algorithms for real-time operation
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formats. Experience with AI and deep learning algorithm development for medical image analysis. Familiarity with SQL, Python, Tensorflow, Scikit-Learn, and Pandas. Additional Qualifications Considered
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control strategies. Develop and implement advanced control algorithms for real-time operation and performance enhancement of power electronic converters and transformer-based solutions. Perform hardware-in
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- “Data-driven algorithms for multi-product inventory systems with non-stationary demand”. Qualifications Applicants should have:- (a) a doctoral degree, preferably in the area of operations management
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a Research Fellow to contribute to a project focused on algorithm design in Game Theory and Fair Division. Key Responsibilities: Formulate mathematical models for research problems in computational
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conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences