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bio materials and porous materials PhD student candidate 2 with background in computer science, AI, machine learning or related fields with the experience in CFD, ANSYS, COMSOL The successful candidates
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, Industrial Engineering, or related discipline; Affinity and/or experience with computer programming, statistical learning, and optimization techniques; A good team spirit and feel at home at the intersection
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comprehensive databases combining nationwide Norwegian health and socioeconomic registry data, biobanks and patient-reported data. Using advanced epidemiological methods, causal inference and machine learning
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
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within the broad topics of modelling tool-workpiece interaction in mechanical material removal processes, zero-defect manufacturing, machining system performance characterization as well as on-machine and
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PhD fellowship in Computer Supported Cooperative Work (CSCW) PhD Project in Novel Computer Science Teaching Methods considering Neurodiversity, Physical Computing, and Universal design in teaching
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or development of machine learning methods, or a desire to learn these skills, are also welcome. We offer the opportunity to work on interesting scientific challenges using modern experimental methods available in
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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-learning energy trading algorithms that are able to cope with these challenges. By leveraging real-time data, developed algorithms continuously adapt to market dynamics and respond to changing market signals
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epidemiological methods, causal inference and machine learning techniques, we aim to: Improve understanding of risk factors for primary headaches Predict diagnosis and disease progression Identify the most