12 molecular-modeling-or-molecular-dynamic-simulation PhD positions at Linköping University in Sweden
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electronic devices The project will include three secondments: University of Bern (Switzerland) – advanced in-operando spectroscopic techniques Molecular Gate (Spain) – advanced methods to image doping
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cancer. The goal will be to find genetic prediction models to be able to predict which childhood cancer patients have a high or low risk of toxicity in childhood cancer. Preliminary the doctoral project
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generative models, geometric machine learning, dynamical systems, and/or multi-modal learning. From the materials science perspective, our primary focus will be on ultra-thin, so-called, 2-dimensional
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level in electrical engineering, electromagnetic engineering, wireless engineering, engineering physics, applied physics, a closely related field. Good command of electromagnetic simulation tools such as
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flow, including circuit design, simulation, layout, PCB design, and measurement of integrated circuits using advanced CAD tools. Excellent communication skills and proficiency in both written and spoken
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to either first-principles calculations or AI supported database management of high-throughput type calculations/simulations. Basic knowledge of density functional theory (DFT) is beneficial. Strong
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embodied AI agents can dynamically adapt to group behaviors and learning needs The project will combine observational studies, interaction design, and experimental evaluations to develop embodied AI-driven
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transcripts, both undergraduate and graduate. If the master’s degree is recent, the applicant must submit a Registrar's letter confirming that they have completed the degree. · A copy of your master's thesis
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address outstanding questions on behavioural evolution in canids. Your work assignments Understanding how behaviours evolve is a long-standing goal in evolutionary biology. Using the domestic dog as a model
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: Verifiable training and trustworthy AI pipelines. Tools for robust data and model provenance in adversarial environments. Methods for protecting training data and end users, including secure data removal and