Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
tools such as RFdiffusion2 or BoltzDesign. Perform molecular dynamics simulations and in silico screening to assess inhibitor-target interactions and predict selectivity. Clone, express, and purify top
-
) the development of a holistic multi-hazard risk framework capturing cascading effects across systems and scales; (2) the creation of digital environments utilizing real-time data for dynamic risk evaluation; (3
-
to engage in world-class research in theoretical nanophotonics. The ideal candidate will be enthusiastic about contributing to cutting-edge research in a dynamic and ambitious young research group, supported
-
with researchers at DTU and KTH, you will help develop an integrated decision-support system that: Uses real-time sensor data and AI models to assess risk scenarios. Dynamically recommends optimal
-
collaboration that covers all aspects of our research: theory and modeling, sample growth and fabrication, experiments and demonstrations. We have created a dynamic research environment of young and senior
-
at the Niels Bohr Institute, Faculty of Science, University of Copenhagen. We are located in Copenhagen. We offer creative and stimulating working conditions in a dynamic and international research environment
-
conditions in dynamic and international research environment. Principal supervisor (PI) is Professor Pernille Bjørn, Department of Computer Science, pernille.bjorn@di.ku.dk The PhD programme Option A: Getting
-
the fabrication of robust artificial quantum superlattices that have never before been realized. Joining our dynamic, international research community, you will gain unique, high-demand competencies in quantum
-
PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
for dynamic risk evaluation; (3) the advancement of risk-to-resilience methodologies; and (4) the establishment of digital twin-based resilience frameworks for CI, HTI, and urban environments. The network
-
that merge thermo-fluid dynamic laws, deep learning, and experimental data. A central goal is to overcome current limitations in TES operation and optimization, enabling discovery of new high-performance and