63 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" scholarships at Nature Careers
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will involve three short visits to partner laboratories to learn cell delivery methods, immunologic characterization of cells and tissues, live cell tracking, and multi-omics data analysis methods. As a
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. An individual career development plan containing research and transferable courses, international research visits, mentoring and career activities, is also a central element. Please visit https://www.interact
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(https://www.cliccs.uni-hamburg.de/about-cliccs/cliccs-ll.html ). In CLICCS-M4, we are further developing the unique ICON-Coast model within the ICON Earth System Modelling Framework. The objective
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Declaration of interest regarding PhD project within the field of biomarker and therapeutic targe...
, or pediatric diseases (prior experience is an advantage but not required) Experience with or willingness to learn biomarker analyses (e.g. ELISA), histological techniques, and molecular assays Interest in animal
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of novel mechanistic insights is gained through the application of novel probabilistic deep-learning models that automatically extract biological and statistical knowledge from your in vivo perturbational
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Research Program RTG3120 on Biomolecular Condensates (https://dresdencondensates.org ). Each PhD project is part of an interdisciplinary framework that includes shared training activities, and supervision by
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catalysts for the synthesis of a range of industrially valuable compounds. This PhD project is part of the Horizon Europe Marie Sklodowska-Curie Action (MSCA) doctoral network (DN) ELEGANCE (machinE LEarning
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, machine learning, or (astro-)physics (in particular cosmology, galaxy formation, or general relativity) will be an advantage. What we offer: Inspiring working atmosphere: You will have the opportunity
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local Danish time Please see the full call, including how to apply, on https://fa-eosd-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/da/sites/CX_1001/job/3497/?utm_medium=jobshare
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of novel probabilistic deep-learning models that automatically extract mechanistic and statistical knowledge from your in vivo perturbational omics data. This interdisciplinary atmosphere has been a main