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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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-performance computing resources suitable for large-scale machine-learning and foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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dramatic upheaval as a result of rapid technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic, and
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remodeling and conformational engineering De novo and semi-rational enzyme design Directed evolution theory and workflow development Library design strategies (focused, combinatorial, and machine-learning
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’ creativity toward concurrent optimization of human health and energy efficiency. This postdoctoral position will help design and evaluate interactive ideation and simulation AI supports tools. You will join an
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data issues. Utilizing machine learning techniques as appropriate for data analysis. Developing computing programs and software to support research initiatives. Applying new methodologies to real-world
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computers lack such abilities. The goal of the Adaptive Bayesian Intelligence Team is to bridge such gaps between the learning of living-beings and computers. We are machine learning researchers with
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are looking for an active researcher with a strong scientific record (experience in leading research projects and completed postdoctoral fellowships is welcome), who is ready to teach courses in physics
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clinical translation. While the group works closely together and with graduate student and postdoctoral trainees, this role is expected to operate with a high degree of independence and take ownership