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for a full-time, on-site PhD position in machine learning, forecasting and time series analysis. Reykjavik University, Department of Engineering. Duration: 3 years. Start date: Negotiable. Reykjavik
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None Additional Preferred Experience working in one or more of the following areas: Longitudinal data analysis Predictive modeling/machine learning models Biostatistics / epidemiological modeling
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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in cancers of unknown primary (CUP). Your Role You will join Subproject 3 (Model Alignment and Optimization), led by PD Dr. Keno Bressem (https://scholar.google.com/citations?user=wIEgwbkAAAAJ&hl=en
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes
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accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy-aware deployment. The role targets foundation models, including large
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Machine Learning - Developing Oral and written communications - Developing Programming Languages - Developing The core technical skills listed are most essential; additional technical skills may be required
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. The positions focus on applied machine learning methods for real-world systems. Possible research directions include: Transfer learning and domain adaptation across heterogeneous production environments (e.g
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database