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to the empirical nature of the project or at least reached within 2 years after starting the PhD program. We are looking for you who are independent, self-driven, and analytical, with an ability to manage complex
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science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and
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Transformation Subject description Artificial Intelligence for Sustainable Transformation is a rapidly growing interdisciplinary research field that plays a critical role in addressing pressing environmental and
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largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence
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diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study
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policies and programmes. These responsibilities include programme review and improvement, accreditation and self-evaluation, assessment of student learning and advancement of student success, academic
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the Data-Driven Life Sciences initiative – one of Sweden’s largest and most ambitious research initiatives, focusing on using data, computational methods, and artificial intelligence to study biological
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policies and programmes. These responsibilities include programme review and improvement, accreditation and self-evaluation, assessment of student learning and advancement of student success, academic
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multiplex analysis. We will assist the computer scientists to apply artificial intelligence Machine Deep Learning models using the omics data of mitophagy to predict risk of cancer and metastasis and design
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for artificial intelligence. We also work with quantum and cryogenic electronics, advanced low-power devices and circuit concepts for the Internet of Things, power electronics, and the design and evaluation