19 machine-learning-modeling-"https:"-"Computer-Vision-Center" positions at Lunds universitet
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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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physics-informed machine-learning models for binding affinity predictions in rational small-molecule drug design. The models will allow prioritisation of candidates from hit discovery through to lead
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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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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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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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%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model
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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
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vision. Understanding or willingness to learn advanced statistical modeling is a plus Assessment criteria and other qualifications: This is a career development position primarily focused on research
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, for that reason we are looking for someone who has: Documented research experience in the design of therapeutic antibody modalities Documented research experience in AI/machine learning/deep learning Documented
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applications, including development and validation of machine-learning and statistical models for disease prediction, prognosis, and therapeutic response. Proficient in R, SAS, and other bioinformatic tools