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Field
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biosciences and data analyses. Qualifications Basic qualifications (required at time of application) PhD (or equivalent international degree) or enrolled in a PhD (or equivalent international degree) program
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subpopulations, as well as (plastic) cancer cell states that contribute to tumor progression, metastasis and therapy resistance. The candidate will lead several projects applying machine learning to (single-cell
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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related discipline. Strong expertise in medical imaging and/or machine learning. Excellent programming and research skills. Interest in translational research and interdisciplinary collaboration with
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related field. Documented expertise in machine learning and time-series modelling (e.g. LSTM, XGBoost, CNN). Strong programming skills in languages such as Python and R. Experience with phenotyping data
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skills (one or more of the following strongly desired) Exploratory analysis of massive datasets (machine learning methods) Spatial data analysis and Geographic Information Systems (GIS) Forecasting and
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. Finally, due to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing
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Master’s degree in data science research, or a comparable domain. A strong background in machine learning (specifically generative models) and/or symbolic reasoning. Ability to conduct high quality academic
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field (e.g. statistics, computer science, or quantitative biology). Experience in the application and development of computational methods/tools or machine learning algorithms. Good computer programming