61 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at Technical University of Denmark
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, modelling and machine learning to improve defect detection, classification and power loss simulations. Benchmarking field-acquired images with laboratory measurements. Publishing results in leading journals
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high-throughput reactor systems, combined with the implementation of a visionary data management strategy, will provide a unique environment for data-rich research supported by machine learning and AI
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Job Description The Machine Learning in Photonic Systems group at DTU Electro at the Technical University of Denmark is seeking a candidate for a PhD position to research multiplexing in photonic
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biology, or analytical workflows. Interest in single-cell analysis, cancer biology, and translational research. Basic level expertise in computational biology (e.g., bioinformatics, machine learning), with
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processing and hybrid BCI design Machine learning (ML) Bioinspired control systems Neuroplasticity and motor recovery Real-time control of soft exoskeletons Your Role As a PhD candidate, you will: Develop and
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as the project manager of our new project DTU-UoN Partnership for Climate Innovation, Entrepreneurship and Education. The aim is to foster learning and knowledge exchange on climate solutions
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of computer-aided tools for chemical and biochemical product and process modeling, process synthesis, design, analysis and operation. The tools are applied in the chemical, petrochemical, pharmaceutical
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. Updating and refining learning components for the 3D-CIRCULAR educational and certification programmes in collaboration with partner institutions. Managing and documenting progress for the second and third
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, thermodynamic models, and unit operations, developed as part of research projects at both CERE and KTC. These software tools include but are not limited to the Integrated Computer Aided System (ICAS), CAPCO2
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industry-facing research collaboration and coordinate cross-partner workflows in planning, data-sharing, and iterative decision-making across industrial and academic stakeholders. Teach and supervise PhD