67 machine-learning-phd Postdoctoral positions at Technical University of Denmark in Denmark
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Electrophysiological signal processing of, e.g., EEG, ECG, EMG, etc. Health data science, incl. modern machine, and deep learning methods, Cloud-based platforms like MS Azure or Google Colab Health data standards, like
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analysis Close collaboration with an interdisciplinary team. Research and teaching efforts at a section and departmental level as appropriate and relevant (e.g., teach and supervise MSc and PhD student
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work, including through writing peer-reviewed scientific article(s) The ideal candidate: Holds a PhD in a relevant field such as epidemiology, public health, biology, or a related discipline. Has proven
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resources Help foster a positive, inclusive, and collaborative group culture, supporting newcomers Communicate results in scientific publications and at international conferences Qualifications Must-have: PhD
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materials 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 300
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that the candidate has demonstrated research experience and is able to participate in international fieldwork, meetings and dissemination activities. As a formal qualification, you must hold a PhD degree (or
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biobased processes to promote a green transition and support the UN Sustainable Development goals. Responsibilities and qualifications You will work with a team of scientists including PhD and MSc students
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microscopy, and mammalian cell culture will be an advantage. As a formal qualification, you must hold a PhD degree (or equivalent). We offer DTU is a leading technical university globally recognized
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PhD students working collaboratively at the interface of physics and Earth science. This project offers a rare opportunity to engage in groundbreaking research with high impact across both academic and
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, including GKP state generation and nonlinear gates. EPIQUE (Horizon Europe): Cluster state generation on photonic integrated chips and its integration into a measurement-based Gaussian Boson Sampling machine