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amplifier performance. By combining advanced device measurements, empirical modeling, and power amplifier design, this project will generate new insights into the material, process, and design factors
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cement for waste containment. You will work with trace amounts of radioactive tracers in an inert gas glove-box and learn to interpret results into parameters used for safety evaluations of waste
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nuclear energy. This position offers you the opportunity to deepen your expertise in radioactive waste management by conducting cutting-edge research on the use of concrete and cement for waste containment
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of protective materials Perform validation studies to ensure computational model accuracy against experimental benchmarks Collaborate on the development of testing methodologies for protective device evaluation
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modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an innovative startup partner. You will be
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Are you passionate about pushing the boundaries of medical research? Join us at Chalmers University of Technology to explore how advanced models can transform the way we study treatments. We
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patterns of genomic sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr. Eran Elhaik to design machine-learning models
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sequences, with applications ranging from biogeographical mapping to paleogenetic reconstructions. The candidate will work jointly with Dr. Eran Elhaik to design machine-learning models that unlock
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. Eran Elhaik to design machine-learning models that unlock the potential of genomics for forensic investigations and historical reconstructions. Work duties We aim to develop machine learning methods
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kinetics Experience with multiple electrochemical methods Experience with radical polymerization chemistry Experience working with hydrogel or soft material systems and/or tissue models And the following