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Remote sensing data understanding Software development of few-shot learning models And will allow you to develop competences in Software management (e.g., Git use) Types of data in remote sensing Use
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models Use of PyTorch and/or HuggingFace ESSENTIAL REQUIREMENTS To be registered as a student in an undergraduate master’s degree programme in Computer engineering, Computer Science or a cognate
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present. Evaluation of the trained models on suitable datasets. What you contribute Good knowledge in the field of machine learning and training neural networks. Good Python skills, preferably some
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fully funded PhD position in the area of safe data-driven system identification for cyber-physical systems, offered by our research group at the intersection of control theory, machine learning, and
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in cancers of unknown primary (CUP). Your Role You will join Subproject 3 (Model Alignment and Optimization), led by PD Dr. Keno Bressem (https://scholar.google.com/citations?user=wIEgwbkAAAAJ&hl=en
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-physics modelling of power electronic systems and components, with special focus of magnetic components, Incorporating physics-driven machine learning approaches in power electronics design, Incorporating
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Hospital, Copenhagen). The successful candidate will be responsible for designing and implementing the predictive modeling strategy of the project. This includes: Developing machine-learning prediction
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modelling, multimodal neuro-imaging and physics-informed machine learning to improve assessment of glioblastoma treatment response. The candidate will also be expected to contribute to the formulation and
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next-generation machine learning (ML) models that are both data-efficient and transferable, enabling more reliable catastrophic risk prediction, defined as the probability of exceeding critical safety
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Science, or related field Knowledge of flexible antennas, wireless communications, and machine learning Good skill set in signal processing and optimization techniques Proficiency in Python for modeling and machine