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or equivalent academic or industrial experience. Experience in handling chemical datasets and tools (e.g., RDKit, DeepChem, Gaussian, ORCA, ChemML). Knowledge of modern machine learning techniques (e.g., deep
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, M. N. Schmidt, S. F. Nielsen, K. Banaszek, D. Zibar, “Mode Mismatch Mitigation in Gaussian-Modulated CV-QKD,” in Proceedings of the European Conference on Optical Communication (ECOC), 2025 - https
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in physics, chemistry, or related field Basic knowledge of ab-initio methods to address molecular excitations (e.g., Gaussian, NWChem, or any other quantum-chemistry software) Knowledge of quantum
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the application of computational fluid dynamics and Gaussian dispersion models (e.g., R-LINE, MOVES, AERMOD) to simulate pollutant behaviour, apportion source contributions, and evaluate mitigation
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and pre-process clinical datasets (handling missing values, outliers, ICD-10 and CPT code grouping) ⢠Implement advanced data imputation methods (e.g., Gaussian Copula)  Machine Learning and Modeling
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, Neuroscience, Physics or a related field. Candidates should have strong skills in machine learning and statistics and experience with Gaussian process regression and/or probabilistic regression. Experience with
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Viklund and will involve the study of one or more of the following areas: Coulomb gases and related areas, 2D random conformal geometry (SLE theory, lattice models, gaussian fields, etc), conformal field
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about the division can be found at https://uol.de/en/ai4health The position is part of the DFG-funded cluster of excellence "Hearing4All.connects". Among its research goals is uncovering
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quantum computing, including GKP state generation and nonlinear gates. EPIQUE (Horizon Europe): Cluster state generation on photonic integrated chips and its integration into a measurement-based Gaussian
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applied questions such as environmental management and risk assessment. For more information on EnvStat, please see https://www.helsinki.fi/en/researchgroups/environmental-and-ecological-statistics TWO