Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Delft University of Technology (TU Delft)
- SciLifeLab
- Ludwig-Maximilians-Universität München •
- NTNU - Norwegian University of Science and Technology
- NTNU Norwegian University of Science and Technology
- Radix Trading LLC
- University of Amsterdam (UvA)
- Fraunhofer-Gesellschaft
- Helmholtz-Zentrum Berlin für Materialien und Energie GmbH
- Karolinska Institutet, doctoral positions
- UNIVERSITY OF VIENNA
- University of Hamburg •
- University of South-Eastern Norway
- University of Vienna
- 4 more »
- « less
-
Field
-
multidisciplinary environments Curiosity-driven and self-motivated working attitude Knowledge of biomechanical modeling, anatomy, vision-based motion capture, machine learning, control systems Keep in mind
-
systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
-
or environmental engineering, Mathematics (Operations research) or Computer Science or Machine Learning). Documented knowledge of relevant methodologies, both quantitative and/or qualitative, at master’s level
-
influencing factors and improve the accuracy, robustness and energy efficiency of intelligent sensing systems. Apply AI as an engineering tool: Use signal processing, statistical methods and machine learning
-
student and postdoc who will carry out experimental work, which will allow for validation of the models. You will collaborate with experts from TU Delft, TNO and several large and SME companies involved in
-
applications based on the following skillsets. If you do not fullfil all requirements, please still apply (there is room for learning-on-the-job). An engineering degree, preferably, aerospace or mechanical
-
chemistry and study the physicochemical properties of peptides loaded into the materials. Build surrogate models and apply machine learning techniques to extract design rules and rapidly screen thousands
-
; multiple courses on topics such as time management, handling stress and an online learning platform with 100+ different courses; 7 weeks birth leave (partner leave) with 100% salary; partly paid parental
-
meet the requirements for admission to the faculty's doctoral programme in Engineering Cybernetics . Strong programming skills, in particular Python, and practical experience with modern machine learning
-
computer simulations Working in close collaboration with theoretical physicists to test models and develop new insights Your Profile Bachelor's degree in physics Master’s degree/diploma in condensed matter