71 post-doc-in-machine-learning Postdoctoral positions at Technical University of Denmark in Denmark
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enable us to test Risø HPP when it is connected to different electrical power grids, which we will emulate with the CGI. We are looking for a Post-doc who will be engaged in experimental research and model
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, retrapping, tunnelling, and recombination, based on their respective transition probabilities. Use machine learning approaches to optimize model performance and run simulations over multiple time scales
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background in modelling, optimization, and control approaches in energy systems, Research vision and potential Familiar with machine learning techniques Background in programming tools such as Python and Julia
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Job Description DTU Aqua (The National Institute for Aquatic Resources, Technical University of Denmark) offers a 2-year Post doc position focused on the functional ecology of phagotrophic
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processing in the human ear. Applying machine-learning models to analyse objective electrophysiological markers, enabling the inference of auditory dysfunction and pathophysiology. Qualifications The candidate
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folding of such proteins. Carry out expression and purification for functional characterization of selected proteins. Teach and supervise BSc and MSc student projects Potentially Co-supervise PhD students
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be obtained from Head of section, Professor Britt Bang Jensen, tel: +45 93518948, mail: abrj@aqua.dtu.dk You can read more about the section for fish- and shellfish diseases at https://www.aqua.dtu.dk
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engineering. Learn more about our work: www.healthtech.dtu.dk/3bs . About the project Blood transfusions save lives—but donor blood is limited, perishable, and not always available in emergencies. Synthetic
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relevant (e.g., teach and co-supervise PhD and MSc student projects). Dissemination of your research through publications in “top rank journals of the field ” and attendance at conferences. Qualification
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electronics, fluidics system, and data collection hardware (based osingle-board computer systems, e.g., ESP32 or similar). Demonstrate prototype applicability under simulated lab-based and real-life conditions