50 postdoc-in-thermal-network-of-the-physical-building PhD scholarships at Technical University of Munich
Sort by
Refine Your Search
-
-situ measurement network and perform terrestrial laser scanning, analyzing microclimate data and their relation to forest structure, and using optical satellite time series and radiative transfer models
-
to perform team-oriented as well as independent work • Very good communication and writing skills are necessary We offer: • A modern workplace in a brand-new building, with top equipment (Cytek Aurora Spectral
-
, metabolomics, and precision health YOUR PROFILE Completed university degree (Master’s or equivalent) in a scientific or technical field such as Physics, Biotechnology, Bioinformatics, Mathematics, Statistics
-
high-dimensional single-cell analysis and within the LPI network (scRNAseq, spectral flow cytometry) to translate fundamental insights into translational applications for human health and disease. We
-
normal physiology and autoimmunity in collaboration with project partners • elucidate the transcriptional networks regulated by FoxP3 and c-Rel in Treg cells and screen for novel critical regulators
-
29.04.2021, Wissenschaftliches Personal This PhD position is in the field of resource management for wireless network that leverage “digital twins” modeling aspects of the physical (such as user
-
duration Payment is according to the wage agreement of the civil service TV-L, 65% of E13 for PhD student positions and 100% of E13 for Postdoc positions. Please note that there are no additional
-
Application process Send your application in English by email to amx@wzw.tum.de with title “Research Associate application” at latest 01.07.2025 including • A cover letter (for example elaborating your
-
, Machine Learning, Hyperspectral Cameras • Professional proficiency in written and spoken English Application process Send your application in English by email to amx@wzw.tum.de with the title “Research
-
learning to push our understanding of the robustness and explainability of Federated Learning models. Your responsibilities: Build and create clinical use-cases for benchmarking existing state-of-the-art