49 senior-lecturer-distributed-computing PhD positions at Technical University of Munich in Germany
Sort by
Refine Your Search
-
experience and seniority (TV EntgO Bund EG 13). To promote diversity, we welcome applications from talented people regardless of gender, cultural background, nationality, ethnicity, sexual identity, physical
-
accessible to users from science and industry Your qualifications: ■ Master’s or equivalent graduate degree in computer science, artificial intelligence, machine learning, mathematics, statistics, data science
-
leading international journals and conferences • Literature research • Scientific publishing Your qualifications: • Completed academic university degree (university diploma / M.Sc.) in Computer
-
insights into the dynamic distribution patterns of human tissue resident T helper cells across space and time. Topic: Dissecting the body-wide spatio-temporal organisation of human resident T helper cells T
-
project partners from academia and industry - Publication of research results in leading journals - Supervision of student projects as well as cooperation in lectures - General lab management and
-
degree in a technical field (mechanical engineering, mechatronics, robotics, electrical engineering, computer science, etc.) -Know-How from lectures in robotics (e.g. environment perception, path and
-
technical field (mechanical engineering, mechatronics, robotics, electrical engineering, computer science, etc.) -Know-How from lectures in robotics (e.g. environment perception, path and behavior planning
-
05.06.2025, Wissenschaftliches Personal Are you looking for an opportunity to shape the future of quantum computing? With superconducting quantum computers on the verge, we aim to strengthen our
-
privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and
-
privacy-preserved fashion. Research topics include, but not limited to, i) handling distributed DL models with data heterogeneity including non i.i.d, and domain shifts, ii) developing explainability and