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Field
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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of induced pluripotent stem cell-based human disease models 2) Understanding the role of immunity during tumorigenesis as well as disease relapses in order to design novel inhibitors, CAR-T cell therapy and
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Informatics, Health Data Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated
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Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including
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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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fellow will be part of a growing team of researchers, postdocs and PhD students working on intelligent observing systems using machine learning and data assimilation methods in the ACTIVATE project. UiO
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knowledge-grounded reasoning with flexible machine learning Tools that reduce manual burden while preserving traceability and clinical interpretability This position offers the opportunity to publish novel
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for this position, the following is required: PhD in data or computer science, machine learning, AI, statistics, mathematics, biophysics, bioinformatics. Additional requirements In addition to your CV and your
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(EHR), health information exchanges, and data analysis software. Experience with health IT innovation, including working with artificial intelligence, machine learning, telemedicine, or mobile health