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, TensorFlow, JAX). Demonstrated ability to work in interdisciplinary teams bridging machine learning, neuroscience, and chemistry. Excellent communicative skills and Collaborative abilities Motivation and
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), research is carried out in computer vision, robotics and machine learning. We are now looking for two postdocs in robotics and machine learning and computer vision. The successful candidates is expected
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together with Jendrik Seipp, Senior Associate Professor in Artificial Intelligence at LiU. The research projects for the advertised position will be in the areas of automated planning and machine learning
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intelligence, machine learning, data science, applied mathematics, or a closely related field, awarded no more than three years prior to the application deadline*. Documented research experience in machine
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are looking for The following requirements are mandatory: To qualify for the position of postdoc, you must hold a doctoral degree in computer science, artificial intelligence, machine learning, data science
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semantic representation models for sign language. Such representations are key to allowing SL to be efficiently processed by large language models (LLMs), and will lead to machine learning models that can
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description and duties The postdoc fellow will conduct research at the borderline between the fields of information visualization / visual analytics as well as machine learning in close collaboration with
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that may also be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organisations, military service or similar circumstances, as well as clinical
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ocean environments, ensure safe and sustainable operations. Our activities are centered on numerical modelling (e.g. CFD, FEA, FSI, optimization, machine learning), but also include experiments and real
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of information visualization, visual analytics, applied machine learning but possibly also in the areas of the domain experts. Within the DISA environment, large and complex data sets from various domain areas