Sort by
Refine Your Search
-
Category
-
Program
-
Employer
-
Field
-
epidemiology to understand RNA metabolism. Perform stochastic simulations to analyze model behaviors. Fit the model parameters to empirical RNA expression and RNA-protein binding data. Predict outcomes
-
and accepted to the PhD program at Stockholm University. Project description Project title: “Deep learning modeling of spatial biology data for expression profile-based drug repurposing”. A new exciting
-
of these cutting-edge research projects and in the societal transformation that they entail. We offer a broad range of courses and study programmes to match the skills in demand. We hope that you will help us to
-
their daily tasks, take part in various projects connected to those systems. You will also be updating P&IDs to match the current system and handle documentation. Our plan is to transfer all P&ID to E-plan so
-
developing synthesis and verification techniques based on, e.g., model checking combined with machine learning, to facilitate guaranteeing safety and security of industrial autonomous systems. The employment
-
investigates inflammation in health and disease using cutting-edge exposure systems and advanced 2D and 3D cell models. In parallel, NanoSafety2 focuses on the toxicity assessment of particle emissions from
-
variants High-resolution cryo-EM and cryo-ET imaging to resolve molecular structure and guide engineering Protein design and molecular modeling, supported by AI tools such as AlphaFold2 Development
-
of these cutting-edge research projects and in the societal transformation that they entail. We offer a broad range of courses and study programmes to match the skills in demand. We hope that you will help us to
-
the context of this position, we will focus on understanding the functional basis of interactions between hosts and their co-infecting viruses in multipartite model systems. We also will develop other host
-
with 25–30 active PhD students at the department. Work duties We are now looking for teaching assistants in FRTF05 Automatic control, basic course (study periods 1 & 2), FRTN65 Modeling and learning from