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data to address priority questions in cancer care pathways, diagnostic delay, and treatment access. The role will involve advanced quantitative analyses, such as survival modelling, machine learning, and
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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 1 hour ago
mechanics, data-driven approaches, and machine-learned interatomic potentials are highly beneficial but not required. We offer The 3-year Liebig PhD Fellowship is tax-free and is funded 1850 euros/month. The
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data to address priority questions in cancer care pathways, diagnostic delay, and treatment access. The role will involve advanced quantitative analyses, such as survival modelling, machine learning, and
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geometry, and/or data science. Specific topics of focus include, but are not limited to, linear response, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidate will work in close cooperation with staff and our current PhD students. PhD research fellows receive
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large, highly diverse and multi-modal datasets (e.g., images, surveys, statistical and sensor data). Familiarity with geostatistical, GDAL, Python, PostGIS/PostgresSQL, Machine Learning, AI, Internet
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combined with machine learning. The long-term aim is to conceive in-silico novel immunodiagnostics and immunotherapeutics using the disease-diagnostic information and therapeutic potential that is directly
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have experience with applications of machine learning and deep learning on medical image data that you have experience applying methods within generative artificial intelligence to medical images and
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in coding e.g. Python, or MATLAB Writing skills for research papers. Experience in applied machine learning, fault diagnosis, laboratory testing and development is a plus. Good knowledge in Norwegian
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. The PhD Fellow will be affiliated with the Computer Networks Research Group (ComNet) at the department. The proposed PhD project aims to advance the field of 6G vehicular networks by harnessing the AI