69 big-data-and-machine-learning-phd Postdoctoral research jobs at Technical University of Denmark in Denmark
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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
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approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modeling, aimed at uncover the key traits that define successful microbial biofertilizers
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that also act as green energy producers driving the societal transition towards net zero. In this position, you will build on your expertise in IoT and low-power computer and communication systems to research
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artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic
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file. The file must include: Application (cover letter) CV Academic Diplomas (MSc/PhD – in English) List of publications Letters of recommendation or alternatively contact information to references
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levels, ranging from BSc, MSc, PhD to lifelong learning students. We have about 300 dedicated employees. Read more about us at www.energy.dtu.dk Technology for people DTU develops technology for people
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is looking for a post doc with a background in seafood science and/or seafood development or processing for a large project in collaboration with Food and Agriculture Organisation (FAO), which is an
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university deep tech startups, carry out independent, high-quality research leveraging this data, and engage with stakeholders in the Danish and international deep tech arena. Responsibilities and
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circular plastics economy. Learn more about the project at https://inano.au.dk/about/research-centers-and-projects/enzync . Note that the current position is based at DTU, Lyngby. Further information may be
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digital co-simulation platforms (e.g., Modelica-Python/Simulink) Applying machine learning and data-driven approaches to enhance the operation of district heating substations Participating in course