114 postdoctoral-position-in-molecular-dynamic-simulation-self-assemble-polymer PhD positions at DAAD
Sort by
Refine Your Search
-
on life events and romantic relationship dynamics. The assessment methods include longitudinal experience sampling studies. Another research focus of the division is the investigation of romantic
-
be found at www.trr-energytransfers.de . We invite applications for a PhD position in the area of Air Sea Fluxes at the Institute of Coastal Ocean Dynamics with preferred starting date 1st June, 2025
-
Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
-
, downstream processing and molecular biology Experience in bioreactor operation Basic knowledge of further processing Interest in working in an international team, enjoy teamwork, good time management and
-
. The Department ‘Anthropology of Politics and Governance’ is offering a position for Doctoral Student (m/f/d) for “an ethnographic study of digital healthcared in Saxony-Anhalt” (starting as early as possible after
-
/f/d, E 13 TV-L, 50-75%) The position is limited for three years. The group deals with the physics of molecular and biological materials. The primary research areas include materials for photovoltaics
-
Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
-
Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
-
Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally
-
challenging, and new 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