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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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research teams from different scientific cultures (ecology, physiology and ethology, chemistry and subatomic physics) develop very high level programs based on scientific instrumentation. The IPHC is
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familiarity with the desperation threshold model of decision making. Where to apply E-mail daniel.nettle@ens.psl.eu Requirements Research FieldEconomicsEducation LevelPhD or equivalent Skills/Qualifications PhD
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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/ijtpp5030016. Required skills / qualifications________________________________ Diplomas : PhD in Engineering, Physics, Theoretical and Applied Mechanics, or Applied Mathematics Experience : None Knowledge
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. This production does not involve any catalysts such as light or heat. It is an intrinsic property of the air/water interface, and therefore potentially ubiquitous in the troposphere. This PhD project aims
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to the comprehensive analysis of field samples and the mathematical modeling of cellular interactions. LPHI includes four teams working on parasite models (Toxoplasma and Plasmodium), thus offering a stimulating working
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ingredients for Earth-like magnetic fields on millennial time scales in dynamo models. The research activities are two-fold. First, the candidate will run numerical dynamo simulations with various combinations
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of glioma extension using magnetic resonance spectroscopy in 7T MRI. Part of this project involves the development of MRI image processing tools, the implementation of mathematical models for the analysis
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to specialize the memory management of several applications, including virtual machines. Running memory management policies in user space opens up new opportunities, particularly the integration of AI models