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, computational biology, statistics or a closely related field. You have strong programming skills, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision
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, theoretical models, and national laboratory operations. Each AI associate works on a few complementary projects, enabling deep focus, impactful results, and cross-disciplinary inspiration. Projects range from
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support for the lab Job Requirements: Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, or related field Knowledge and experience in world model, computer vision and deep learning
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply
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out a rigorous scientific study aimed at comparing the performance of deep learning models in detecting complex visual anomalies. Take charge of the entire study, define the evaluation criteria and
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with deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills in Python and experience with a deep learning framework such as
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Assistant Professor/Associate Professor/Professor (Open Rank), School of Law – Health Policy and Law
law; as well as a deep understanding of law and policy as it relates to Medicare, Medicaid, the Affordable Care Act, universal health insurance; and public health challenges and societal health risks
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of the recruitment and description of the project The Computational Intelligence Lab targets NLP, Time Series Forecasting/Generation, Knowledge Processing using Deep Learning. Applications are expected to construct
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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or