134 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" positions at DAAD
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
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
-
in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the
-
and further information go to: https://www.uni-muenster.de/CiM-IMPRS We offer 16 fully financed PhD positions. More positions financed by work contracts may be offered depending on availability
-
), 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
-
methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team orientation excellent
-
start? Have a first look of what it’s like working at ICE-2: https://go.fzj.de/ice-2 We are offering an interesting PhD Position – Techno-economic assessment of geothermal plants with material co
-
equivalent in Health Economics or a related field and speak English. If you want to join us, please apply until March 29, 2026 via our online application platform. https://www.drs.fu-berlin.de/user/register
-
(including cover letter, CV, diplomas/transcripts, etc.), which you can submit via our online-applicationsystem: https://www.hzdr.de/db/Cms?pNid=490&pLang=en&pOid=76687
-
. Only online applications will be accepted. AVAILABLE PROJECTS: Nanoscience: Application of bistable DNA devices Biophysics: Learning in living adaptive networks Biophysics: High-resolution structural
-
the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data