Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
-
Field
-
. The label is a token of the University's commitment to continuous development of the position and working conditions of researchers according to the guidelines set forth in the European Charter
-
the return of regional industrial policy. The project develops new innovation and growth policy tools by combining research on innovation and industrial policy with research on strategic regional development
-
resistance and microbiomes, statistical analysis of high-dimensional datasets, and developing bioinformatic pipelines for high-throughput analysis in high-performance computing (HPC) clusters. The work
-
. The application areas are diverse, ranging from quantum devices to medical technologies. Overall, modeling and simulation tools developed in Computational Physics have increasing role in fundamental sciences, and
-
manipulation of developing mouse organs. The project will focus on how signaling pathways operate at the intersection of growth control and branching morphogenesis in the developing mammary gland and will use
-
, inclusion, and well-being. Group members have ample opportunities to develop both technical and transferable skills—including programming, data visualization, communication, networking, and leadership
-
opportunity to develop your professional skills in a versatile operating environment. In your work, you will be part of a large group of doctoral researchers, other researchers, and supervisors in the doctoral
-
for the duration of the project, access to modern laboratory facilities, and strong support for professional development, including the possibility to attend international conferences and training courses. In
-
. We will develop an isotope version of a process-based CH4 model and update the representation of different wetland types in the model using a data inversion approach. Additionally, we will analyze
-
Control Strategies for Autonomous Driving (HUMINAC). The project focuses on developing novel, human intention–aware control strategies at the intersection of machine learning, reinforcement learning, and