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CMFI Cluster of Excellence iFIT Cluster of Excellence Machine Learning CIN LEAD Graduate School & Research Network Collaborative Research Centers Transregional Collaborative Research Centers (CRC-TRRs
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Helmholtz-Zentrum Berlin für Materialien und Energie | Berlin, Berlin | Germany | about 2 months ago
publication in renowned scientific journals. Your Profile University degree (Master's/Diploma) in physics, mathematics, data science or related fields Solid knowledge in machine learning/statistics and first
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CMFI Cluster of Excellence iFIT Cluster of Excellence Machine Learning CIN LEAD Graduate School & Research Network Collaborative Research Centers Transregional Collaborative Research Centers (CRC-TRRs
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and interpretable quality assessment algorithms. This research combines various machine learning topics, including uncertainty, explainability, and fairness in supervised and unsupervised deep learning
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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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Machine learning Experience is ideally shown through a thesis, seminar papers, or scientific publications. Alternatively, excellent grades in a respective Master’s programme. Strong intrinsic motivation
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, engineering, natural sciences or other data science/machine learning/AI related disciplines Language requirements English C1 or equivalent Application deadline January, please see website for exact date Submit
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of computational approaches to large scale simulations Basic knowledge of (geo)chemical processes and machine learning will be of advantage Expertise in Machine Learning approaches, ideally beyond neural networks
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research areas. New insights and synergetic effects resulting from collaboration between inherently different viewpoints of separate fields typically accompany this endeavour. Our task force on machine
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and