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. Familiarity with frameworks such as TensorFlow and Keras, as well as libraries including Scikit-learn, NumPy, and pandas; - Experience with machine learning models such as Extreme Learning Machine (ELM
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or electrochemical system PhD in Chemistry/Materials Science/Physics Encourage initiating activities on MOF development, devising, and analytical process Experience in machine learning will be preferred Good oral and
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reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability
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focuses on the development of secure and trustworthy AI for resource-constrained embedded systems used in power electronics and energy infrastructure. The research will investigate how machine learning
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computational electromagnetics and electromagnetic simulation techniques. Experience in AI-based RF transistor modelling is highly desirable. Solid knowledge of machine learning algorithms and their application
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-2025” concerning security-threatening activities against Sweden. Graduates in the spring of 2026 may also apply Where to apply Website https://www.bth.se/english/vacancies/job/phd-student-position-in
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of results at conferences interaction with team members and international collaborators Required skills : Degree : PhD in computer science, machine learning, or computational biology We expect a candidate with
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areas: Developing and training robust machine learning surrogates to replace computationally expensive high-fidelity simulations, enabling exploration of vast design spaces. Formulating optimization
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EngD: Physics-based AI for Intelligent Machining: Learning, Optimisation, and Uncertainty in Next-Generation CAM Software (sponsored by DigitalCNC) EPSRC Centre for Doctoral Training in Machining
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liquid handling systems; and interest in machine learning and AI. 3/23/2026