246 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Germany
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reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming languages (C++, Python, or Julia) is highly relevant. Knowledge
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of climate model output by means of classical statistical and machine-learning methods #coordination of scientific workflows among project partners Your profile #Master's degree and PhD degree in meteorology
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external forcings on climate analysis of climate model output by means of classical statistical and machine-learning methods coordination of scientific workflows among project partners Your profile Master's
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: According to §110 (4), sentence 3, the BerlHG provides academic staff with an appropriate amount of time within their working hours for their own further academic qualification. What we are looking for PhD
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areas is expected: numerical analysis, scientific computing, model reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming
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Max Planck Institute for Biology Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 28 days ago
once a year to meet with the consortium. The position is for two years, with the possibility for extension. Requirements PhD (or equivalent international degree) or enrolled in a PhD degree program (or
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fusion energy research Work in an excellently equipped research centre with cutting-edge laboratories and on-site computer clusters No teaching requirement, you can spend your working time for research
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reports Develop and apply software tools Document software and data according to the FAIR data principles Contribute to workshops and training activities Your Profile: Master with subsequent PhD degree in
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for the production of heat pipe components Your Profile: Completed university degree in mechanical engineering, physics, materials science, computational engineering, or a related field, with a PhD or Master degree
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease