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of scientific data, e.g. from image acquisition modalities or scientific simulations. Efficient algorithms are at the core of most of these data analysis and visualization applications. The focus of this Ph.D
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with machine learning and generative AI algorithms, with working knowledge of deep learning frameworks such as PyTorch or TensorFlow is considered a strong advantage. • Extensive experience in multi
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are of particular merit: Experience with organic (bio)electronics, implant design, and/or conducting polymers (ideally all areas of expertise listed) Experience with protein chemistry and investigation of enzyme
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CST Microwave Studio, HFSS or EM Pro for antenna modeling and design is required, as is experience with programming languages like MATLAB, Python, or similar for antenna array analysis and algorithm
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and development of algorithms, methods, and theories aimed at better understanding the properties and underlying mechanisms within statistical and deep learning-based systems also in the presence
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like computational complexity of algorithms. It’s also fairly common that we need to drill down into the code for some tool to figure out what’s wrong, so being able to read and understand code is
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degrees and universities at which they were obtained, a list of peer-reviewed publications (if any), and contact details of at least two reference persons. Certificates of degrees and undergraduate
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include the design and implementation of finite element multiscale models and machine learning algorithms, analyzing related experimental data, and collaborating with industrial collaborators to validate
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professors and lecturers, doing research in diverse topics such as ethology, genetics, microbiology, conservation biology – and, of course, theoretical ecology. The division carries out undergraduate education
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cancer. The goal will be to find genetic prediction models to be able to predict which childhood cancer patients have a high or low risk of toxicity in childhood cancer. Preliminary the doctoral project