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health data, such as electronic health records or biobank-scale resources (e.g., UK Biobank, All-of-Us, FinnGen). Familiarity with machine learning approaches, such as penalised regression, deep learning
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, or related fields Strong background in computer vision and sensor fusion Experience with deep learning frameworks (PyTorch, TensorFlow) Knowledge of 3D perception systems (RGB-D cameras, LiDAR) Familiarity
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or sensory biology is required and a PhD degree or similar is an advantage. Prior experience with machine learning, acoustic analysis, image analysis, stereo video 3D reconstruction, signal processing and
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also be able to demonstrate excellent ability to code with or learn computer programming languages, such as C++, C#, Python, and/or Matlab. A desire to engage in cross-disciplinary research
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extraction) that can be miniaturized and integrated into portable devices. Perform SERS measurements and data analysis of SERS data (e.g., using machine learning). Develop, test and apply new fiber-based SERS
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science applications Computational Atomic-scale Materials Design with a focus on materials modeling and discovery with electronic structure calculations and machine learning Luminescence Physics and
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, a large initiative funded by the Danish Ministry of Foreign Affairs and managed by Danida Fellowship Council. Ethio-Nature aims to optimize the use of machine learning and remote sensing to site