108 machine-learning-"https:" "https:" "https:" "https:" "U.S" PhD positions at Nature Careers
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applicants in accordance with European and German legal regulations. Further information on data protection and the processing of personal data can be found at: https://www.isas.de/en/datenschutz . The closing
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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 experience and are willing to learn new techniques. References may be contacted and interviews may be conducted to determine your potential fit within the existing team, with emphasis placed on interpersonal
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is accessible online: https://www.tec21.fr/phd-tec21 What we offer : 7 fully funded PhD positions with employment contract, competitive salary (monthly estimate net salary between 2 050 and 2 150 EUR
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(XRD) to characterize, at the molecular level, smectite samples from various Swedish mineral deposits. The PhD student will be part of a research group active in the area of molecular geochemistry (http
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websites. Application Process Applications for both programs must be submitted online by January 14, 2026: https://www.uni-goettingen.de/de/application/556704.html Applicants will be asked to upload a CV
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fields: nonlinear PDEs, functional analysis, numerical analysis, PDE-based modeling and numerical simulations excellent command of the English language ability to teach in German and English We offer
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their achievements and productivity to the success of the entire institution. At the Faculty of Electrical and Computer Engineering, Institute of Solid State Electronics, Chair of Coating Technologies in Electronics
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of publications (applicants applying for the position as senior researcher should indicate scientific highlights), H-index and ORCID (see http://orcid.org/ ) Teaching portfolio including documentation of teaching
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machine learning approaches to quantitatively analyze experimental data and predict emergent multicellular behaviors under varying mechanical and chemical environments. For more information about our lab