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the national Data-Driven Life Science (DDLS) program. About the position and the project As an industrial PhD student, you will be employed by the startup company PredictMe AB while being formally enrolled as a
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written English, ability to work both independently and collaboratively. Additional qualifications Experience or coursework in one or more of the following areas is considered an advantage: formal methods
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missing physics rules to interpret experimental observations. The GLYCOCALYX Network will train 15 PhD Fellows in chemistry, physics and biology methods and concepts required to resolve the dynamic
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principled new models and methods, for modern machine learning problems. Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models
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look forward to receiving your application! Do you have a background in machine learning and interested in telecommunications? You have a chance to contribute to development of sensing methods for new
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investigates how individuals and communities in Central Asia pursue justice and rights outside formal legal channels and institutions, using informal negotiations and everyday social interactions. It explores
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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experimentally driven (approximately 70/30 wet lab to modeling) and will include: Design and fabrication of 3D-printed brain tissue models with tunable transport properties Development of experimental methods
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the flexibility of neural methods. If successful, the work has the potential to advance applications such as automated theorem proving, knowledge-graph inference, and causal analysis. The Department of Computing
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methods that reduce compute, energy usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and