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Overview Eine Druckversion dieser Ausschreibung (PDF) steht Ihnen in den weiteren Registerkarten (z.B. „Bewerbungsvoraussetzungen“) zur Verfügung. A print version of this announcement (PDF) is available in the further tabs (e.g. “Application requirements”). Programme Description The Pre-Doc...
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collaborative team in an outstanding scientific environment. Qualifications The successful applicant (m/f/d) will hold a Ph.D. in computer science, machine learning, mathematics, bioinformatics or related fields
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Job Offer from May 15, 2024 The Max Planck Institute for Physics is a research institute focusing on particle and astroparticle physics from both an experimental and a theoretical perspective. One main research activity is the participation in the ATLAS experiment at the Large Hadron Collider...
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mathematics, physics, machine learning, computer science or the geosciences can carry out research independently with curiosity, creativity, initiative and persistence works well in a small group with diverse
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to cells. Naturally, this research is performed in a highly interdisciplinary manner and involves physics, physical and bio chemistry, cell biology and mathematical data analysis. The RTG 2756 offers a
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masters degree and subsequent Ph.D. degree in Computer Science, Mathematics, Physics Engineering or in a similar field. Alternatively an excellent masters degree with professional experience Very good
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the modelling pipeline Your Profile: A university degree (Master) with subsequent PhD in Applied Mathematics, Mathematical Biology, Computational Physics/Engineering or related fields with strong relation
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) #write, publish and present research in scientific journals and conferences Your profile #PhD in mathematics, physics, machine learning, computer science or the geosciences #can carry out research
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techniques, modern sequencing technologies and bioinformatics and statistics Knowledge in programming (e.g. with R, Python, Pearl) and mathematical modelling with special focus on the field of microbial
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with mathematical modeling and machine learning methods will ultimately allow us to predict the entire recognition space for any given TCR sequence. Our work is embedded into close collaborations with