22 machine-learning "https:" "https:" "https:" uni jobs at Lunds universitet in Sweden
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the progression of ARDS in intensive care patients with sepsis. To enable this, we will develop information-theoretic machine learning methods to determine which protein interactions are driving disease progression
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statistical mechanics. The Computational Biochemistry group consists currently of eight coworkers and combines quantum chemistry, statistical mechanics and machine learning with biochemistry, medicinal
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students. The rest of your time (40%) is devoted to teaching. The department has developed several courses within data science, e.g., Bayesian methods, Advanced Machine Learning, Deep Learning and AI methods
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data at an internationally competitive level. Experience of biostatistics or machine learning approaches Proficiency in a scripting language like R or Python, as well as ability to work efficiently in a
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the design of antibody modalities Documented experience from cancer-related research Documented research experience in AI/machine learning/deep learning Documented experience in the development of therapies
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from information and coding theory, machine learning, and distributed algorithms. The project is in collaboration with Linköping University, which includes opportunity for research visits. The project is
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machine learning is desirable but not required. Proficiency in written and oral communication in English. Relevant educational background, for example, in economic history, economic demography, economics
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biology platform https://www.scilifelab.se/units/structural-proteomics/ The unit provides access to cutting-edge equipment and expertise, for the analysis of protein interactions and conformational dynamics
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of staff and running costs will be provided. You will receive five weeks of training in teaching and learning in higher education and get the opportunity to learn Swedish through the University’s Swedish
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that are commonly used today. Using the improved noise models, machine learning methods will be used to enhance the segmentation of EEG data into auditory signal and background activity allowing for refined control