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characteristic classes by global currents obtained from generalized Monge-Ampère type products. The project has connections to both commutative algebra and complex geometry. About the department of Mathematical
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Lund University was founded in 1666 and is repeatedly ranked among the world’s top universities. The University has around 47 000 students and more than 8 800 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human...
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, mathematics, physical geography, and physics. The Faculty of Science is located in the northern part of the University campus. We have around 1,500 students, 360 doctoral students and 700 staff. Our departments
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, Culture and Communication (UKK) our students mainly study programmes relating to teacher education, but we also offer programmes and courses in languages and communication, mathematics/applied mathematics
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engineering sciences, architecture, technology-related mathematical sciences, natural and nautical sciences, working in close collaboration with industry and society. The strategy for scientific excellence
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-based methods to improve cancer therapy Your profile Qualifications PhD in bioinformatics, computer science, biology, medicine, or mathematical statistics. Experience in cancer research and analyses
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in control of grid-connected converters and/or system stability. Strong analytical and mathematical skills, particularly within small-signal modelling and analysis. Documented laboratory experience
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Lund University was founded in 1666 and is repeatedly ranked among the world’s top universities. The University has around 47 000 students and more than 8 800 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human...
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Computer Science, Mathematics, (Bio)Physics, Engineering, or equivalent qualifications/experience Demonstrated proficiency in web application development using frameworks such as FastAPI, flask or React.js
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at the intersection of numerical analysis and scientific machine learning, focusing on the development of reliable, physics-aware AI frameworks. The aim is to build a mathematically grounded approach for approximating