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-scale inverse problems by combining interpretable high-level probabilistic models, multi-physics data integration, and modern machine learning. The resulting methods will be validated on groundwater
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) Familiarity with mathematical programming solvers (e.g., Gurobi, CPLEX) or probabilistic/Bayesian computing frameworks (e.g., Stan, PyMC) Ability to formulate high-impact, novel and well-defined research
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data science approaches includes the application of Bayesian inference or probabilistic machine learning to geophysical models. UiO is subject to the Security Act, which governs the organisation's
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) (PDF) under the protocol established with +ATLANTIC - “Dynamic downscaling of European CAMS air quality forecasts to high resolution over Portugal”, CV 181/2024 of Centre for Environmental and Marine
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questions in cancer biology. Learn more about Dr. Bild’s lab here. As a successful candidate you will: Build probabilistic models of tumor dynamics from serial ctDNA and tissue samples. Develop deep learning
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data science approaches includes the application of Bayesian inference or probabilistic machine learning to geophysical models. UiO is subject to the Security Act, which governs the organisation's
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Experience with population genetics or statistical genetics Familiarity with Bayesian methods, probabilistic modeling, or graphical models Experience with scientific computing in Python, JAX, Torch, Julia, C
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opportunities within the company. Responsibilities Develop and implement advanced computational and machine learning strategies, including deep learning, graph-based methods, and probabilistic modeling
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generation algorithm based on different approaches to improve understanding the behavior of forecasting algorithms in time series and tabular data. The workplan will be as follows: Literature review Design of
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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