106 programming-"the"-"DAAD"-"EURAXESS"-"IMPRS-ML"-"O.P"-"CeMEPI-PGE" positions at UNIVERSITY OF VIENNA
Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
actively participate in the training of archivists, further develop the training program in consultation with the archives, coordinate external teaching, and conduct innovative research in the field
-
skills in archaeological science or organic chemistry, high motivation and enjoyment of research. Excellent command of written and spoken English. You are expected to subscribe to the PhD program of
-
the team! It is that easy to apply: Letter of Motivation (Academic) Curriculum vitae Final Degree certificates (Certificate of completed diploma or master's degree programme) List of publications
-
of Romance Studies offers Bachelor’s, Master’s and PhD degree programs in five Romance languages and teacher-training courses for secondary schools in three languages (French, Italian and Spanish
-
. Experience performing and publishing academic research at peer-reviewed conferences and journals is a plus. You are an experienced programmer, preferably using the Python programming language. You are
-
students and participate in examination activities within the Bachelor and Master programs Journalism and Communication Studies. You participate in evaluation measures and quality assurance. This is part of
-
of your research will lie on the study of topological order using tensor network methods. You continuously stay informed about the state of the art in your field. You present your research plan
-
. The Department of Romance Studies offers Bachelor’s, Master’s and PhD degree programs in five Romance languages and teacher-training courses for secondary schools in three languages (French, Italian and Spanish
-
publication-ready habilitation. You hold courses independently in the Bachelor programme „Languages and Cultures of South Asia and Tibet” and the Master programme “Languages and Cultures of South Asia” within
-
. program and will work on the development and analysis of statistical methods for machine learning, particularly in the context of high-dimensional models and with a particular focus on methods such as