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an emphasis on technology, data science and the humanities. We are seeking highly motivated individuals with a strong interest in cancer genetics and genomic medicine to join the research team under Associate
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of the designed algorithms and systems. Help with research presentation works such as high-quality paper writing. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering
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Fellow in Autonomous Systems and Control to design and implement efficient, performance‑guaranteed distributed control approaches, leveraging cutting‑edge learning algorithms and AI strategies
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models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in machine learning, computer vision, and medical
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within/outside of research group, using appropriate presentations format Key Competencies and Requirements: PhD degree in bioinformatics or statistical genetics, computational biology, or related field
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algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
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Responsibilities: Development of stochastic and analytical methods for nonlinear partial differential equations Implementation of relevant numerical experiments using deep learning algorithms Job Requirements: PhD
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studies using genetically modified mouse models, human islets, and multi-omics approaches. This position is ideal for researchers with a strong interest in lipid metabolism, autophagy/lipophagy, and
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an emphasis on technology, data science and the humanities. We are seeking a highly motivated individual with a strong interest in cancer genetics and genomic medicine to join our research team under Associate
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or Mathematics or Statistics with a strong background in one or more of the following: AI, machine learning, Bayesian statistics, programming languages, logic and algorithms, formal methods, probability and