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creative and stimulating environment with the opportunity to network with both the business community and international contacts. Read more about our benefits and what it is like to work at SLU at https
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three lab courses. These method-based courses last up to five days and constitute an important element of the study programme, with students receiving intensive training in the latest methods and
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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and analysing general Computer Vision methods, using real-world image data as a challenging experimental setting. Research focus The PhD will address open research questions in Computer Vision related
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by colloids, as well as methods for immobilizing these ions. Modern methods of theoretical chemistry (first principles, kinetic Monte Carlo, machine learning) will be applied to investigate diffusion
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signal processing methods and a modelling environment, aided by unique hardware-in-the loop, to assess the detection and estimation algorithm performance and determine optimal multistatic configurations
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required to take part in at least three lab courses. These method-based courses last for up to five days and constitute an important element of the study programme, with students receiving intensive training
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 18 hours ago
that enables them to work on an innovative research topic and to develop and apply the scientific methods appropriate to their research topic. Course organisation During the three-year programme, doctoral
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twin of sperm motility, and utilize it to develop a separation method. Your tasks will include: Performing computer simulations and matching them to experimental data Very close collaboration with
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, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on methods that reduce compute, energy