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Field
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), microbiological, immunological, bioinformatical (genomics and transcriptomics) and other techniques. This will involve both in vivo and in vitro research and include handling zoonotic bacteria. Learning Objectives
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Center for Drug Evaluation and Research (CDER) | Southern Md Facility, Maryland | United States | 2 months ago
to exploration of machine learning and deep learning approaches for tasks such as process monitoring, fault detection, real-time release testing, and the prediction of quality attributes from process data
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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, implementing, and optimising advanced AI algorithms, with deep proficiency in machine learning architectures, scalable model development, and high-performance code. The role holder will have the opportunity
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of Oslo’s Department of Informatics (IFI) and is hosted by the Network and Distributed Systems Research Group (ND) with co-supervision from IFI’s Machine Learning section and the University of Inland
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Computer Science, Artificial Intelligence, Software Engineering, or a related field. Strong programming proficiency in Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch
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on projects at the intersection of computational neuroscience and machine learning. This position is part of a multi-investigator grant on the role of memory in intelligence systems. The Postdoctoral Fellow
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competence that meets the requirements for a position as associate professor in Norway, NTNU will arrange for you to acquire such competence during the employment period. In such cases, you will also be
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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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: Probabilistic generative models (VLMs, diffusion, flow models) Reinforcement learning & Markov decision processes Causal inference & counterfactual reasoning Mechanistic & physics-informed modeling Agentic AI