104 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at DAAD
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), the sorption of PFAS and heavy metals onto natural nanoparticles will be investigated in situ using a dedicated field exposure method developed by our team, complemented by laboratory experiments and machine
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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individually, e.g., through training and networking opportunities specifically for doctoral candidates (JuDocS): https://go.fzj.de/JuDocs FAIR REMUNERATION: Depending on your qualifications and assigned
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courses or doctoral candidates Selection Selection is made by the National Science and Technology Council - NSTC Further information National Science and Technology Council - NSTC https://www.nstc.gov.tw
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. Only online applications will be accepted. AVAILABLE PROJECTS: Nanoscience: Application of bistable DNA devices Biophysics: Learning in living adaptive networks Biophysics: High-resolution structural
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at gsnas.fu-berlin.de Required Documents Required Documents Motivation letter CV Certificates Research expose References Language certificate Application Application https://www.jfki.fu-berlin.de/en
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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(see https://tu-dresden.de/ing/maschinenwesen/postgraduales/promotion?set_language=en ) knowledge in EcoDesign, climate sciences and life-cycle assessments project management knowledge of the host
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arrival date of the university central mail service or the time stamp on the email server of TUD applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a
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. Only online applications will be accepted. AVAILABLE PROJECTS: Nanoscience: Application of bistable DNA devices Biophysics: Learning in living adaptive networks Biophysics: High-resolution structural