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of small cryptic plasmids in the development and spread of antibiotic resistance, and ii) Use machine learning tools to examine the complex interplay between bacterial hosts, various plasmids and resistance
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hampers our ability to establish causal relations between molecular alterations and disease phenotypes. In this PhD you will address this by developing a deep learning model of cancer. The PhD position
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on applying AI and machine learning to molecular design challenges. This position is one of several industrial PhD roles funded by the DDLS program, which supports training in four strategic areas: cell and
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biology. The applicant should also have an interest in learning, or previous experience in, computer programming, particularly using languages such as Python. The ideal candidate is driven and a creative
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in computer science, engineering, data sciences, applied mathematics, machine learning, or another related field; or Have completed at least 240 credits in higher education, with at least 60 credits at
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Wiberg is “Innovative statistical and machine learning methods for comparing performance and outcome in register data studies”, with overall aim to develop, evaluate, and implement innovative statistical
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given to the ability to assimilate third-cycle courses and study programmes at a higher education. The applicant should have documented knowledge in energy systems and machine learning technologies
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machine learning and computer vision techniques to enhance data analysis, pattern recognition, modeling, and prediction. The role requires a solid understanding of fluid dynamics and heat transfer, as
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passion for learning and a desire to work in a multidisciplinary and open team. Contract terms and what we offer The PhD-positions are fully funded from start As a PhD student at Chalmers, you are
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dynamics simulation and controls toolbox fascinating? The research of the PhD student will touch upon various topics multi-body dynamics, optimal control theory, machine learning and robotics and artificial