Sort by
Refine Your Search
-
Listed
-
Employer
- Chalmers University of Technology
- SciLifeLab
- Lulea University of Technology
- Linköping University
- Uppsala universitet
- Karolinska Institutet
- Umeå University
- Luleå University of Technology
- Mälardalen University
- University of Borås
- University of Lund
- Blekinge Institute of Technology
- KTH Royal Institute of Technology
- Karolinska Institutet, doctoral positions
- Nature Careers
- 5 more »
- « less
-
Field
-
essentially corresponding knowledge in another way. Experience with Computational Fluid Dynamics (CFD) is advantageous, as is knowledge of quantum chemical calculations (DFT). An interest in machine learning
-
the sustainable companies and societies of the future. Our Cybersecurity group is now looking for a PhD student in Cybersecurity with specialization 6G and satellite communication to contribute to our
-
bioinformatics, systems biology, machine learning, or biostatistics, with a focus on practical applications and problem-solving. Terms and conditions The doctoral student will be employed on a doctoral studentship
-
and machine learning, will be employed and, if necessary, specifically developed. What do we offer? A creative and inspiring environment full of expertise and curiosity. Karolinska Institutet is one
-
methodologies, ranging from material characterization, via machine-learning and high-throughput methods, to ab initio calculation of electrochemical reaction kinetics. The position is part of the Chalmers Area of
-
goal oriented and persevering in your work. Candidates will be assessed upon their ability to: acquire knowledge within the fields of control theory, robotics and machine learning, independently pursue
-
advantageous, as is knowledge of quantum chemical calculations (DFT). An interest in machine learning and AI-based methods, as well as programming skills in languages such as Python or MATLAB/Simulink, is
-
properties of superconducting circuits, both analytically and numerically. Familiarity with open quantum systems. Background in optimal control methods. Experience with machine learning for optimization