Sort by
Refine Your Search
-
to two PhD students in Statistics for the research project “Next-Generation Latent Variable Models for Social Data Science”. Application deadline: November 4, 2026. We are seeking one to two PhD students
-
combine ultrafast pump–push–probe experiments with sub-10 fs resolution, finite-element simulations, and quantum models extending the Tavis–Cummings Hamiltonian. The aim is to demonstrate coherent control
-
and emerging threat models. studying the security notions, attacks, and proofs for hash-based signatures, with a focus on physical attacks. Admission requirements The general admission requirements
-
. The primary objective of this PhD project is to develop adaptive statistical models for marked spatial and spatio-temporal point processes. Many real-world systems exhibit substantial spatial heterogeneity and
-
spectroscopy (XANES, EXAFS, and others) at synchrotron facilities such as MAXIV with theoretical molecular modeling of the corresponding systems. The work will be carried out in the Molecular Geochemistry
-
-resolved and multimodal X-ray absorption spectroscopy (XANES, EXAFS, and others) at synchrotron facilities such as MAXIV with theoretical molecular modeling of the corresponding systems. The work will be
-
modeling. The knowledge generated in the project will form the basis for new methods and tools to better analyze household energy behaviors. Particular focus is placed on identifying barriers and variations
-
Arabidopsis thaliana or a comparable model plant system. Very good written and oral English language skills. Meriting qualifications The following qualifications are not required at the point of appointment
-
involve a combination of computational and experimental approaches including mammalian tissue culture, genetic engineering, proteomics and animal infection models. A person who is employed as a PhD student
-
Familiarity with spatial analysis, GIS, or geospatial data workflows. Experience with machine learning, modelling, or systems analysis approaches Interest in resilience, sustainability, and environmental