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                fast-paced research environment, a structured and organized approach is highly valued. You will work in a team of researchers from diverse backgrounds, including PhD students and postdocs, and should 
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                , simulation, layout, and PCB design using advanced CAD tools, as well as experience in the measurement and characterization of integrated circuits. Strong communication skills and proficiency in both written 
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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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                CMOS technologies. Well-versed in the end-to-end design flow, including circuit design, simulation, layout, PCB design, and measurement of integrated circuits using advanced CAD tools. Excellent 
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                trustworthy and factual language models. Host of the largest basic research program in Sweden Wallenberg AI, Autonomous Systems and Software Program (WASP) as well as the Wallenberg AI and Transformative 
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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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                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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                applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery 
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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