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Field
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the use of machine learning methods to process complex data sets. The focus is on techniques such as ultrasound, radar, computed tomography, acoustic emission analysis, and infrared thermography
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) with an interest in ecology and evolution Demonstrated experience with mathematical modelling, and an ability to learn new concepts and (computational) methods as needed Affinity with scientific computer
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, modelling, and AI to turn scientific research into real-world impact. You’ll join an open, supportive environment that fosters learning and professional growth. Job requirements Must haves: Master’s degree in
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-connectivity Communication system modelling, performance analysis, and simulation. Optimization tools and machine learning techniques. Hands-on experience with software-defined radios (SDRs) and/or
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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modeling and computational workflows Knowledge about machine learning: statistics and deep learning Experience in data analysis, visualization and presentation Good programming skills in languages such as
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cell (and one cell–cell interaction) at a time. You will work with large-scale single-cell and spatial transcriptomics data to develop and apply single-cell foundation models — generative machine
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cell (and one cell–cell interaction) at a time. You will work with large-scale single-cell and spatial transcriptomics data to develop and apply single-cell foundation models — generative machine
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the individual process parameters on the target properties and develop predictive machine learning model; iii) based on the machine learning algorithms, develop PBF-LB Mg alloy with defined microstructure
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machine learning We offer: Academic freedom to pursue your scientific interests related to infection biology, inflammation, gene expression, and intracellular organization Competitive salary including