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biological models, an international consortium of leading colorectal cancer scientists, clinicians and mathematicians will explore the mechanisms of evolved adaptive response to therapeutic selective pressures
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of agent-based and/or continuum modelling, analysis and simulation. You will have experience of programming in Python and C++, or demonstrated ability to rapidly acquire fluent knowledge of new programming
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) and Aurélien Quiquet from the Laboratoire des Sciences du Climat et Environnement (LSCE), an expert of the GRISLI model, in interaction with the PhD student on the ANR Delta project (coordinated by Y
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are encouraged to apply. Where to apply Website https://www.academictransfer.com/en/jobs/354423/postdoc-project-in-vitro-models… Requirements Specific Requirements Qualifications: PhD degree in neuroscience
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experience in quantitative modelling, preferably using R. Is fluent in English (spoken and written) and has good communication skills. Is enthusiastic about developing replicable models to address complex risk
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results and absence of an explanatory condition) taken as a model of medically unexplained symptoms in adulthood. Analyses will be conducted using data from the French CONSTANCES population-based cohort
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ecosystem functioning. The postdoctoral project will focus on analyzing and modeling tree architecture in relation to forest vitality and stress predisposition. The work will combine 3D structural data (from
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-PDF), data analysis and modelling for partially crystalline material systems Developing lab-based X-ray multimodalities: X-ray diffraction & imaging presentation of scientific results through
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Strong understanding of causality concepts, biases, randomized trials, and trial emulation (target trial emulation) Familiarity with standardized data models (e.g., OMOP-CDM) Genuine interest in applied
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for analysis of complex systems, in particular towards the identification of regime trends and tipping points Deduce hypothesis on the principles of system evolution and development Build computational models