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. The postdoc will apply and optimize new approaches for NGS library preparation and analyze genomic data for variant calling. We value diversity of backgrounds in our team and seek candidates who can commit to a
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/Fellow to undertake applied research related to the design and optimization of fuel-cell-based electric vertical take-off and landing uncrewed aerial systems for medical deliveries. Reporting to ARC Future
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tools that enable the energy-optimal, cost-efficient and safe design and operation of future energy systems. Your Job This PhD position focuses on scenario development, as well as the modelling
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developments, you will work closely with the other PhD, postdocs, engineers in the lab, and other collaborators from TUM and ETH, to establish a digital twin for construction, and optimize CAML hardware
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
professionalism, enhance learning, and create personal and professional sustainability. We optimize our partnership with the UNC Health System through close collaboration and commitment to service. Position Summary
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plenary sessions to guiding individual students or small project teams. In this role, you will serve as a role model for PhD candidates within the group. Where to apply Website https
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-design transformation in Nordic and European electricity markets. The PhD candidate will develop and apply optimization-based market models to analyze how different market-clearing and capacity-allocation
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factors on pre-optimization and post-surgical outcomes, and various clinical and radiographic factors associated with surgeries for adult degenerative and deformity conditions. Position: Spine Research Fall
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and 3D finite element modelling (COMSOL, CST) of the optical control process for materials. -Integration of the optimal compositions into simple test structures to directly evaluate electrical switching
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representations of the radio environment limits the ability of networks to anticipate connectivity disruptions and optimize resource allocation proactively. This PhD project addresses these challenges through