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ocean environments, ensure safe and sustainable operations. Our activities are centered on numerical modelling (e.g. CFD, FEA, FSI, optimization, machine learning), but also include experiments and real
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, or erroneous data, Data cleaning and generation, Development of enhanced loss functions and information-theoretic methods for optimized data analysis, Machine learning-based image segmentation of tomographic
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– protein interactions or enzyme optimization. Main responsibilities The successful candidate will use and develop methods within one, or preferably multiple, of the following categories: Sequence library
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device processing to optimize different important properties, such as high frequency operation, output power, linearity, and efficiency. The goal is to explore the limitations of III-nitride semiconductor
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decision-support tools for energy-aware planning, predictive maintenance, and resource optimization, -use robotics, autonomous systems, IEC 61499, and digital twins to design and evaluate distributed control
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maintaining systems and services, building scalable pipelines, optimizing performance, and ensuring high quality in both data and models. We are looking for individuals who are passionate about technology and
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, this project aims to establish new knowledge on how microbial proteins can be optimized and integrated into hybrid foods of the future. About us The Department of Life Sciences aims to bridge cutting-edge life
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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to ensure optimal tissue concentrations during surgery. The PhD student will utilise national and international arthroplasty registry data, adapt in vitro diagnostic tools such as the Minimum Biofilm
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. Strong machine learning fundamentals (probability, statistics, optimization) and strong interest in time-series modeling and physics-guided machine learning. Proficiency in Python and modern deep learning