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project. The project will also employ a PhD student at Lund University, focusing on developing hybrid architectures for deep learning-based image processing and methods for multimodal medical data. We will
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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. Such methods are founded on mathematical models and algorithms from the field of mathematical optimization. The dose planning is based on medical images and a treatment protocol, and it is evaluated by using key
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. They have led to a plethora of important downstream applications, such as image and material generation, scientific computing, and Bayesian inverse problems. At the core of these models are differential
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assignments Your main tasks will be to analyse and compile data and video material from a random sample of Swedish dog owners. The long‑term goal is to create a more complete picture of the Swedish dog
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of English. It is an advantage if you have: Experience of working with large text or image materials, for example organising digital archives, curating public databases, structuring and quality-assuring large
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is on fundamental limits, and development of algorithms and methods. Applications can be found in, for example, signal, image and video processing for autonomous vehicles and swarms of drones; massive
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of existing bioinformatic workflows and development of new pipelines. The analyses will be carried out on GPUs and part will consist of data processing and visualization in order to facilitate interpretation
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, machine learning, signal processing, and control engineering. Experience in implementing and integrating different methods in complex systems is considered meritorious. You should be clear in your
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application! Your work assignments Spatio-temporal processes are everywhere in science and engineering, with applications ranging from weather prediction to cardiovascular medicine. Developing machine learning