Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
and textiles, in order to identify application fields with high environmental sustainability potentials. This PhD position is part of the project FERNS (Holistic integration of eco-Friendly dEsign tools
-
of adaptive radiation and associated key innovations in the evolution of freshwater diatoms. By integrating morphology, physiology, genomics, transcriptomics, and computational modeling, we aim to (i) determine
-
57,000/year Collaboration with an internationally recognized research team with leading experts in remote sensing, atmospheric modelling, emission quantification, and machine-learning. Integration
-
well-integrated into the institutional activities and participate in regular team meetings. You will work closely with the project leaders and other team members. The goal is to produce high-quality
-
recognized research team with leading experts in remote sensing, atmospheric modelling, emission quantification, and machine-learning. Integration into Empa's Atmospheric Modelling / Remote Sensing group with
-
instrument for in-situ assembly of 2D heterostructures, opening new possibilities to study strongly correlated electronic phases and magnetic order. The tool will integrate transport, optical spectroscopy, and
-
integrated in a removable smart denture. The project targets the integration an advanced micro-system comprising sensors, micro-fluidics, and a sophisticated drug delivery system. The complex 3D printed
-
poorly understood. This project seeks to address this question by examining the role of adaptive radiation and associated key innovations in the evolution of freshwater diatoms. By integrating morphology
-
focus on developing innovative multifunctional coatings with integrated antimicrobial properties. Key objectives include: Design and synthesis of multifunctional antimicrobial coatings. Materials and
-
secondments at UCL – University College London and at University of Leeds (UK). We are looking for you! Do you want to be trained to develop multi-level thrombosis risk prediction models by integrating insights