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motivated to move the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our team, you get the opportunity to use the latest algorithms in machine learning
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 2 months ago
challenges facing society today. At the Institute of Radiopharmaceutical Cancer Research scientists (f/m/d) from the fields of physics, chemistry, biology, pharmacy, immunology, medicine and IT develop
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review. You will possess master level quality, accuracy, speed and knowledge for your assigned specialist area and execute reflex testing algorithms. Work schedule: 100% FTE, Fixed Duration Appointment
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Description Primary Duties & Responsibilities: Implements: Algorithms and computer software for analyzing omics-based data sets [high-throughput, massively parallel genomic/proteomic/clinical]; Data management
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the Generative Flow Network (GFlowNet) algorithm. We plan to further enhance this algorithm to consider the shape of biological binding sites and incorporate modules for optimizing physicochemical properties
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the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our group, you get the opportunity to use the latest algorithms in machine learning for improving
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Location: Cambridge, Massachusetts 02142, United States of America [map ] Subject Areas: Machine Learning Artificial Intelligence Biology Appl Deadline: (posted 2025/09/30, listed until 2026/03/30) Position
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biology, and expertise in computational methods, data analysis, software and algorithm development, modeling machine learning, and scientific simulation Ability to work well in an interdisciplinary
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algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology, and expertise in computational methods, data analysis, software
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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning