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epidemiological methods, causal inference and machine learning techniques, we aim to: Improve understanding of risk factors for primary headaches Predict diagnosis and disease progression Identify the most
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patients Experience with clinical data collection Familiarity with epidemiological methods and registry-based research, epigenetic analyses or machine learning. Interest or experience in science
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registry-based research, epigenetic analyses or machine learning. Interest or experience in science communication and public engagement Experience with publishing biomedical papers Experience with open
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desirable: Advanced variable/feature selection (e.g., LASSO, Elastic Net, Random Forests, wrapper methods). Dimensionality reduction/feature aggregation (e.g., autoencoders, PCA, manifold learning). Pattern
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will be adapted to the candidate’s background and the evolving needs of the center. Possible directions include the application of rock physics models, Bayesian inversion methods, and machine learning
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physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or equivalent) in
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properties of the Higgs boson. The group focuses on final states containing several tau-leptons. The analysis activity is now extended to include generic anomaly searches using Machine Learning. Furthermore
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anomaly searches using Machine Learning. Furthermore, the group takes part in ATLAS upgrade, with participation in the ITk-Pixels project, with responsibilities concerning testing and delivery of pixel
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machine learning. Magnetic Resonance Imaging. Laboratory experience from porous media research related to physics and/or chemistry. Personal and relational qualities will be emphasized. Motivation
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machine learning Personal and relational qualities will be emphasized. Motivation, ambitions and potential will also count when evaluating the candidates. Special requirements for the position