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hazards, enhancing asset protection, maritime security, emergency preparedness, and societal resilience. The project will leverage advanced AI and machine learning techniques to enable predictive risk
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-omics liquid biopsy data for minimal residual disease (MRD) detection, quantification, and assessment. This project will involve applying and evaluating statistical and machine learning models for data
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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP
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a weighting of 60%; (as defined in tables A.2.1, A.2.2, and A.2.3, in the criteria minutes) A.2.1: Knowledge of computer vision, weighted at 70% A.2.2: Knowledge of machine learning, weighted at 15% A
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veterinary technician simulation training as well as internal and external continuing education. This experience will provide the foundation necessary to: identify the learning needs of diverse audiences
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team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/erapid-multiplexed-electrochemical-sensors-for-fast-accurate-portable-diagnostics/. What you’ll
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highly interdisciplinary setting combining microbial mutagenesis assays, mammalian cancer models, next-generation sequencing, bioinformatics, and machine learning. Experimental data will be integrated with
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reference. The student will focus primarily on the photonic integration of machine learning methods, contributing equally to the development of ML algorithms in this context. Their work will include
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/drc/ ). About the role The role will contribute to on-going research at the UCL Hawkes Institute to develop advances in computational modelling of neurodegenerative disease, machine learning, and big