26 machine-learning-phd Postdoctoral positions at Technical University of Denmark in Denmark
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in advancing chemical impact assessment through machine learning - DTU Sustain Kgs. Lyngby, Denmark Posted on 06/06/2024 Be the First to Apply Work with international front-runners in developing
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focus on (i) generative models and machine learning of structure-property relations (ii) crystal point defects for quantum technology applications (iii) excitons and nonlinear optics. You will work in an
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of machine learning methods for reduced order process model generation (experience with Gaussian process models is further desirable). Developing APIs and automated workflows for connecting numerical
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Excellent communication skills and fluent in English, oral and written Familiarity with analysis of metagenomic data and machine learning techniques is a plus but not essential. Experience with data
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Skip to main content. Profile Sign Out View More Jobs Postdoc position in advancing chemical impact assessment through machine learning - DTU Sustain Kgs. Lyngby, Denmark Be the First to Apply Job
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discovery, materials processing, and structural analyses. We also focus on educating engineering students at all levels, ranging from BSc, MSc, PhD to lifelong learning students. We have about 270 dedicated
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organization of large datasets. Experience in machine learning with limited data is preferred. As a formal qualification, you must hold a PhD degree (or equivalent). We offer DTU is a leading technical
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that take into account their sensory and perceptual profiles. This project connects key principles of the neuroscience of perception, visual computing, human computer interaction and machine learning
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techniques. Located in the Intelligent Transportation Systems group in DTU (http://mlsm.man.dtu.dk ), this role is ideal for candidates with a PhD in simulation, mathematical modelling, machine learning, and
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the research community at conferences and workshops. You should bring the following qualifications: A PhD degree in (computational) neuroscience, biomedical engineering, computer vision, machine learning