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participants to engage in thematic Hackathons and access the Proof-of-Concept testing campaign. The successful candidate will: - undertake testing at LIR-NOTF to the winners of the hackathon for proof
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data
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develop an analytical framework to achieve the grant objectives. Specifically, the postholder will write and publish scientific papers that test whether local extirpation is prompted by the same
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and individual building level, directly contributing to reducing network congestion in the Netherlands. Your work will have real-world impact: the models you develop will be tested and validated
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be addressed to Dr. Norbert Hajos, nhajos@iu.edu. Additional Qualifications Experience in behavioral tests using mice, AAV injection and/or fiber photometry is favorable. Knowledge of using DeepLabCut
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scale. Key activities include: • Fracture mechanics and fatigue for bio-based composites • Numerical implementation and optimization. • Model calibration and validation. • Prototype deployment and testing
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multiomics to bridge mechanistic understanding from the neuron to the brain-wide circuit level. You will work on high-resolution mapping of network changes during neurodegeneration and test interventions aimed
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the mesh refinement and mathematically prove that the resulting algorithms converge optimally with respect to the number of mesh elements. You will further implement and thereby validate the theoretical
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close to completing, a PhD in chemistry, physics, materials science, engineering or a related discipline. You will have experience in the electrochemical testing of Li-ion batteries or related energy storage
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for new research directions. The post-holder will be encouraged to think critically about experimental design, develop and test hypotheses, and integrate data from multiple approaches to address fundamental