Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
materials on textiles. Develop and optimize plasma deposition processes for tailoring material properties for specific textile functionalities (antibiofilm, repellence, adhesion). Characterize the deposited
-
contributions are: Conduct applied research on atmospheric pressure plasma deposition of functional materials on textiles. Develop and optimize plasma deposition processes for tailoring material properties
-
their results on advanced equipment. For further information, you may check: wwwen.uni.lu/snt/research/spacer and www.spacer.lu The candidate should develop the following tasks:
-
in microbiome and bioinformatics Develop scientific methodology and techniques adapted to the research project Present research work in leading international conferences Key Skills, Experience and
-
also supports the development of AI-enabled network orchestration, as well as digital-twin-assisted planning for future IoT and 6G systems. You will be mainly in charge of: • Contributing to the design
-
educational sciences. People from across 20 disciplines are working within the Faculty. Along with the disciplinary approach a very ambitious interdisciplinary research culture has been developed. The faculty's
-
experience in the following areas: Applied Machine Learning for Autonomous Systems: Experience developing and deploying ML models for perception, prediction, or decision-making in autonomous driving or robotic
-
heterogeneous multi-omics datasets. Integrative Data Analysis: Perform and lead analysis of large-scale multi-omics datasets, including RNA/DNA sequencing, methylation, and metabolomics. Method Development
-
3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation, remote object ranging
-
, particularly those affected by REM sleep behavior disorder (RBD), a high-risk group for developing PD. Using cutting-edge technologies including iPSC-based dopaminergic neuron modeling, single-cell