293 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Nature Careers
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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activities Present results in international conferences and workshops Your profile A PhD degree in Computer Science, Physics or a related field Strong background in understanding plasma physics and
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and explainable hybrid Artificial Intelligence, i.e., the mix of formal knowledge representation and reasoning with sub-symbolic data-driven machine learning approaches, to work on car-driver digital
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage
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, visualization, and development of new computational methods for processing and analysis of large-scale omics data. We welcome applications from candidates with pure computational background and those combining
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-of-the-art in SLAM, situational awareness, computer vision, machine learning, robotics, and related fields Developing and implementing innovative solutions, validated through real datasets and experiments
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. For more information regarding the position or details of the project, please contact Chloé B. Steen (chloebs@uio.no ). Qualifications Completed PhD in computational biology, bioinformatics, computer
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The key skills and experience of a Senior Research Officer include: Shall possess an MD or PhD degree in immunology, gastroenterology, genomics or another related field Experience with computational
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scaling and generalization behavior Roll out the model to the global user community Requirements PhD or MSc in computer science, physics, mathematics or a related discipline Experience with large-scale HPC
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training