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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
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. Qualifications Required Qualifications: Completed PhD in biomedical engineering, electrical engineering, physics, or a medical imaging related field. Experience with developing advanced pulse sequences
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, Experimental Research, Game Theory, and Artificial Intelligence. Some of the research topics that excite us as a group include: (i) Ethics of AI and Automation; (ii) Social Media Analysis (iii) Algorithmic
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of research and education, covering all aspects of computer science, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking
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of the extraction and beneficiation system. This work will require an understanding of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict
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of Oxford. The post is funded by the National Institute for Health and Care Research (NIHR) and is fixed term for 24 months. The researcher will develop multi-sensor 3D reconstruction algorithms to fuse
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About Us We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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, including artificial intelligence, machine learning, data sciences, algorithms, databases, cloud computing, software engineering, networking, operating systems and security. Job Description The successful
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computing, computer architecture, programming models and high performance computing. These are your qualifications: Must-haves: Completed doctoral/PhD studies in Computer Science or a closely related field
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for the processing and analysis of multimodal spatial biology datasets. Design and apply advanced image analysis algorithms for cell segmentation, feature extraction, and debarcoding for lineage tracing. Statistically