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to: • Have a PhD in nuclear physics or a related field. • Have experience in experimental technique related to ion and atom manipulation at low energy. Knowledge in laser spectroscopy and/or mass spectrometry
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) Proportion of work : Full time Workplace : Institut de Recherche Mathématique Avancée, Analysis group, Université de Strasbourg Desired level of education : PhD Experience required : Recent PhD (less than 4
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systems to ensure the compatibility with the ultra-high vacuum conditions of the storage rings. Finally, the candidate will participate to the supervision of PhD, master and undergraduate students. -Data
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MERMAID. His/ner role will be the compilation and statistical analysis of material/pictorial evidence of fishing from Mediterranean archaeological sites, as well as participation in the preparation of a e
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interdisciplinary training in soft matter, non-linear physics, biology and statistical physics. The proposed research and training programme will focus on the study of smart composite materials to address
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, spatial metabolomics, and genomics data. (S)he will be responsible for developing and implementing computational algorithms and statistical methods to identify key molecular drivers of skin cancers
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technologies to perform this scientific project with high clinical relevance. Requirements. Applicants should have a PhD in any biological science with experience in animal experimentation. Excellent written and
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/Qualifications PhD in Bioinformatics, Computational Biology or a related field. Strong programming skills (e.g., Python, R, Perl, or Java) Proficiency in statistical analysis and data visualization Previous
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supportive and vibrant environment, we value teamwork and diversity and we actively help promote the career of our PhDs and postdocs. Sorbonne University is a world-class, research-intensive university
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Ecole Nationale Supérieure des Mines de Saint Etienne | Saint Etienne, Rhone Alpes | France | 3 months ago
Goals (SDG) 11-Sustainable cities and communities and 13-Climate Action. Its work environment is characterised by high Faculty-to-Student, Staff-to-Faculty and PhD-to-Faculty ratios, as