53 algorithm-development-"Multiple" PhD positions at Technical University of Munich in Germany
Sort by
Refine Your Search
-
10.03.2023, Wissenschaftliches Personal We are looking for a motivated Ph.D. student or post-doc interested in developing novel processes that boost the production of bio-based liquid fuels and
-
messengers transported by the flow or even the pressure of the fluid itself. In an interdisciplinary team, you will either develop theoretical models of the feedback between flow and network architecture
-
that explores biotechnological solutions to the environmental, ethical, and sustainability challenges of conventional animal agriculture. Our research focus is the development of alternative proteins
-
. The Institute of Turbomachinery and Flight Propulsion is pursuing research in technologies of future aero engine architectures for the next generation of aircraft. In this context, the development
-
academic supervision from Prof. Henkel. You will participate in the doctoral program of the TUM School of Management; after about a year, there is the possibility to apply for the School’s Academic Train-ing
-
and optimizing building designs will be developed. The goal of this project is to develop a methodology for automatically checking building designs against regulations, and then provide reasoning about
-
paid PhD position in the area of Natural Language Processing starting as soon as possible. Your responsibilities Research & development projects in the area of Software Engineering. Possible areas
-
paid PhD position in the area of Natural Language Processing starting as soon as possible. Your responsibilities Research & development projects in the area of NLU and NLG Contribution to teaching on
-
in Earth Observation develops innovative methods for information extraction from remote sensing data in close cooperation with the Department EO Data Science of the Remote Sensing Technology Institute
-
private machine learning: Differential privacy (DP) is the gold-standard for privacy protection, but deep learning models trained with DP suffer from privacy-utility trade-offs. You will develop novel model