235 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Germany
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Max Planck Institute for Biology Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 13 hours 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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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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its detailed analysis through Oxford Nanopore Technologies (ONT). Your role will be central in creating and applying bioinformatics and machine learning tools to analyze long-read data and decipher cap
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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
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Identify new applications for Machine Learning in science, engineering, and technology Develop, implement and refine ML techniques Implement parallel ML training on the High Performance Computers Engage in
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The Fraunhofer Institute for Molecular Biology and Applied Ecology IME conducts applied life sciences from the molecule to the ecosystem. It is our vision to pave the way to a sustainable future
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, you must have a PhD in a relevant field. As a suitable candidate, you have expertise in deep convective cloud processes and experience with scientific data analysis. Prior experience in applying machine
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, WES/WGS, targeted NGS, DigiWest), a broad spectrum of molecular and cellular biology techniques, biochemistry and biophysics, computational drug repurposing, and de novo design of novel protein-based
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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