46 software-defined-network-postdoc PhD scholarships at Technical University of Denmark in Denmark
Sort by
Refine Your Search
-
developing novel quantum photonic devices. Such quantum devices are central for quantum information networks, quantum computation, and quantum cryptography systems, and lie at the basis of a forthcoming
-
PostDoc in the project). Collaborating with fellow researchers across the UPLIFT network, including those focused on digital twins at DTU Chemical Engineering- As a PhD candidate, your work will adapt
-
CAT3D, a five-year Villum Young Investigator project that fully funds this PhD position, along with two postdoc positions, we will tackle this grand challenge by fabricating and studying electrocatalysts
-
for species previously limited by technical or financial constraints. This technically ambitious project sits at the forefront of evolutionary and biodiversity genomics, with the potential for field-defining
-
energy modeling and analysis to be part of the Ports as Energy Transition Hubs (POTENT) Marie Sklodowska-Curie Actions Doctoral Network. The network will consist of 15 PhD candidates interested in
-
student, you will focus on mining large Actinomycete genomic, transcriptomic and metabolomic datasets to investigate their transcriptional regulatory network. You will develop and apply the latest
-
directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non
-
characterization of glycoside hydrolases, and a postdoc working on computational modelling of the same enzymes. The PhD focuses on ligand-observed NMR analyses and other relevant methods to provide insight
-
well organized, structured, self-driven and enjoy interacting and collaborating with colleagues including PhD students, postdocs, and you are expected to take part in supervision of BSc and MSc students
-
or experience in strong collaborations and interdisciplinary work at the intersection between machine learning, geophysics and acoustic data modeling. A strong experience with software defined radio Automatic