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19 Mar 2024 Job Information Organisation/Company Forschungszentrum Jülich Research Field All Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country Germany Application Deadline 14 Apr 2024 - 22:00 (UTC) Type of Contract To be defined Job Status Other Is the job funded...
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Your Job: Our team is dedicated to creating a generic numerical solution framework for SOCs, utilizing an open-source approach, and implementing the innovative concept of a “digital twin.” This framework aims to provide a comprehensive understanding of the transport phenomena occurring within...
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will support IAS-9`s data science and machine learning activities, working in a multidisciplinary team of enthusiastic data scientists, software developers, and domain scientists. Your responsibilities
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Infrastructure? No Offer Description Work group: IAS-9 - Materials Data Science and Informatics Area of research: Promotion Job description: Your Job: You will strengthen the data science and machine learning
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non-von Neumann computing hardware blocks for AI, optimization, probabilistic computing, and scientific computing. Machine learning, deep learning and algorithm development, co-designing with mixed
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to different domains. Specifically, you will: Develop, implement, and refine Machine Learning (ML) techniques for self-supervised Deep Learning (DL) for scientific and large-scale datasets Implement parallel ML
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scientific computing. Machine learning, deep learning and algorithm development, co-designing with mixed analog-digital circuit blocks Associative computing from both a circuits and algorithms perspective
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research. We are looking for an enthusiastic researcher who is quick to grasp new concepts and ideas and can solve complex deep learning problems with high-quality software solutions. Experience with large
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Infrastructure? No Offer Description Work group: JSC - Jülich Supercomputing Centre Area of research: Promotion Job description: Your Job: You will join the Simulation and Data Lab `AI and Machine Learning
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to identify degrading enzymes from different data resources Using Hidden Markov Models and similar tools as well as machine learning for the identification of novel and better enzyme variants from a