Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
PhD Position in Theoretical Machine Learning – Understanding Transformers through Information Theory
Join us for a fully funded PhD position in theoretical machine learning to uncover how and why transformers work. Explore their inner mechanisms using information theory. As part of this project
-
supervised by Sebastian Throm. The subject area of the announced position covers kinetic theory, non-local diffusion and dynamics on graphs. The precise research direction will be determined together
-
We are offering a PhD position in the field of algorithmic graph theory. The position is a full-time employment with a competitive monthly salary and full social benefits for up to five years. You
-
of justice, knowledge and power in agriculture and forestry, natural resource management and development in rural areas. Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en
-
application! We are looking for a PhD student in theoretical physics with a focus on the theory of magnetic materials. Your work assignments Your tasks will be to carry out research using advanced theoretical
-
is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture. The ongoing societal transformation and large green investments in
-
. The student will work in a group addressing all these challenges, developing new AI-based methods to improve biological realism in simulations which will lead to more accurately inferred GRNs from real data
-
supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/ . Your work assignments
-
of waveforms for satellite communication and radar systems communication/radar system performance analysis using theory and simulation field tests in relevant operating conditions retrieval of geophysical
-
theory (DFT) and related computational methods. Your work will contribute to predicting and deepening our understanding of electronic, structural, and magnetic properties at solid-state surfaces and