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Field
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genomic, epigenomic, and fragmentomic data, from patient liquid biopsy samples Design and evaluate deep learning models for MRD detection and characterization Collaborate with multidisciplinary teams across
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Experience with analysis of time-series of electrophysiological data Deep learning frameworks (e.g. PyTorch, TensorFlow) Personal skills Enthusiasm and motivation for experimental work Curiosity and
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., deep learning methods, multimodal AI) for the automatic identification of behavioral cues during ecological interactions with people and the environment and analyses of video and speech/language data
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computational science. You will be expected to participate in both computational and experimental activities. This postdoctoral research project will focus on reinforcement learning methods for generating complex
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functions to work properly. Please turn on JavaScript in your browser and try again. UiO/Anders Lien 1st March 2026 Languages English English English PhD Research Fellow in Deep Learning for Medical Imaging
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development through emerging deep learning techniques is of strong interest. The candidate will also evaluate and integrate existing tools and databases into high-throughput pipelines, and facilitate
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.) during medical imaging and radiation therapy treatments. At least one position will be offered to a researcher that has experience in deep learning and AI development and a willingness to apply these
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have high-quality peer-reviewed conference/journal papers in deep learning, and natural language processing fields. Applicants are invited to contact Prof. Boris Ng at telephone number 2766 4021 or via
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clinical approaches, including: Histopathology and digital pathology (whole-slide imaging, WSI) Quantitative analysis of the tumour immune microenvironment AI-based image analysis, machine learning and deep
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measurements of single neuron activity and/or local field potentials Experience with analysis of time-series of electrophysiological data Deep learning frameworks (e.g. PyTorch, TensorFlow) Personal skills