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Field
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that turns experimental plans from the Bayesian optimization engine into coordinated instrument actions, tracks their execution, and returns structured results and status. Define the interfaces (APIs, message
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groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative projects with other group members and our
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theoretically, in tight collaboration with experimental groups. The theoretical methods include stochastic modelling, MD simulation, and Bayesian inference; the position will also include joint collaborative
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. Experience with uncertainty quantification, Bayesian inference, inverse modelling, parameter estimation, or model calibration. Experience with high-performance computing, surrogate modelling, reduced-order
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About the lab The Laboratory of Causal Systems Immunology combines causal inference, probabilistic AI and large-scale in vivo perturbation experiments to uncover how genes shape immune-cell states
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. The position focuses on developing integrated circuits and hardware systems that merge sensing, signal processing, and inference directly at the analog and RF interface. This approach enables orders-of-magnitude
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large language models (LLMs), natural language processing (NLP), and/or machine learning, with a verifiable track record (e.g., publications, thesis, or open-source contributions). Proficiency in Python
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be related to the characterization and circular performance assessment of building envelopes in Europe, with a scope ranging from construction details and building archetypes to large-scale inference
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to work) in reduced order modeling, Causal inference and High Performance Computing are desirable. We particularly encourage applicants with expertise in Multi-scale Modeling, Evolutionary Computation
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Missouri University of Science and Technology | Rolla, Missouri | United States | about 2 months ago
to have experience in several of the following areas: data processing, statistical analyses, R software, regression models, process-based models such as DSSAT or APSIM, Bayesian statistical analysis