Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
requirements: background in environmental science, geography, economics, computer science, quantitative social or political science, physics, ecology or related fields knowledge of climate research ability
-
, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in
-
Computing and Data Science, which is currently being established at JLU. At JLU, the professorship contributes to teaching at a rate of two semester hours per week. You will be responsible for teaching
-
the programme area ‘Plant Adaptation’ (ADAPT). The aim of the research project is to understand how intrinsically disordered regions (IDRs) and prion-like domains (PLDs) control the temperature responsiveness
-
server infrastructure for data analysis interdisciplinary working environment and very good conditions for developing your scientific career and networks doctorate within a structured program The DSMZ
-
standards in biodiversity text analysis Disseminate research results through peer-reviewed publications, academic conferences, and collaborative research proposals Your Profile MSc in biodiversity informatics
-
computer skills for text and image processing (Word, Excel) Experience with statistical analysis programs like R Experience with image editing and processing software like ImageJ, Photoshop Experience with
-
the Research Group “ Numerical Mathematics and Scientific Computing“ (Head: Prof. Dr. V. John) starting September 1, 2025. The position is assigned to the research project "Randomization of Surrogates
-
, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in
-
is using state of the art machine learning tools to extract interpretable latent dynamics. We seek a highly motivated PhD student to develop a predictive computational model using recurrent neural