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on these considerations, solutions will be developed and implemented to create an advanced testing platform for the comprehensive evaluation of the long-term stability of invasive brain-computer interfaces. Your task will
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existing technologies, right through to the tested prototype. The Data-based Methods team at Fraunhofer ENAS develops real-world applications using AI, machine learning and computer vision. The main focus is
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already been fully automated in house. For object types that cannot be reliably reconstructed with classical image-based methods, such as transparent or highly reflective materials, we leverage state
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of the developed applications and contribute significantly to their continuous improvement What you bring to the table Completed master's degree in computer science or related fields of study At least 2 years of
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Germany Application Deadline 30 Nov 2025 - 22:59 (UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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engineering, physics, mechanical engineering, transportation, or a related field Interest in an application-oriented thesis Basic knowledge of numerical calculation methods and fluid mechanics Knowledge
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different sampling strategies (e.g., rejection sampling, importance sampling, MCMC) Implement the chosen methods efficiently in PyTorch, focusing on scalability and numerical stability Benchmark
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architecture, parallel processing and hardware-oriented applications and optimisations as well as operating systems. The project will be carried out in close cooperation with Audi in Ingolstadt and Neckarsulm
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and non-equilibrium systems • Very good academic performance, especially in numerical simulation methods, statistical mechanics, and stochastics • Very good oral and written English skills. For further
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Deadline 19 Jan 2038 - 03:14 (UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff