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Field
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methods and resources at the highest level and advance the IOM's strategic goals. Experience in the field of artificial intelligence (machine learning etc.) is advantageous, a focus on artificial
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ranges from core areas of computer science and electronics over medical applications to societal aspects of AI. SECAI’s main research focus areas are: Composite AI: How can machine learning and symbolic AI
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and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods
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2019, unites top PhD students in all areas of data-driven research and technology, including scalable storage, stream processing, data cleaning, machine learning and deep learning, text processing, data
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is currently the main focus. Here, laboratory experiments are usually combined with state-of-the-art methods such as optogenetics, connectomics or machine learning. Activate map To activate the map
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defects of smectic-liquid crystal order in developing cross-striated muscle, or use machine-learning to expand existing custom-built image analysis pipelines (Python, Matlab). To learn more about this
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or a comparable course of study Good Python and/or Java programming skills Machine learning knowledge and experience Experience with Static Analysis is recommended Good language skills in German and/or
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existing quantitative methods in international nuclear safeguards with state-of-the-art artificial intelligence/machine learning (AI/ML) algorithms Contribute to standardising methodologies for calculating
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bioinformatics and machine learning, we are offering a position for a highly motivated postdoctoral fellow with significant experience in bioinformatics, ideally in the field of cancer research. The position is
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motivated PhD students, interns, and PostDocs at the intersection of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service