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mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. Experience in applying or developing machine learning
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corresponding knowledge in another way. A successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms
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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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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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culture, drug treatments, genetic manipulation, immunoblots, immunofluorescence, and metaphase spreads analysis. As postdoc, you will principally carry out research. A certain amount of teaching may be part
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culture, drug treatments, genetic manipulation, immunoblots, immunofluorescence, and metaphase spreads analysis. As postdoc, you will principally carry out research. A certain amount of teaching may be part
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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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multiple subfields represented, including animal behaviour, evolution, ecology, genetics, zoology, conservation, microbiology and animal welfare. See: https://liu.se/en/organisation/liu/ifm/biolo
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research, undergraduate and postgraduate education within the field of biology, with multiple subfields represented, including animal behaviour, evolution, ecology, genetics, zoology, conservation
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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