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Field
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, and openly release evaluation code. What is Required: A recent Ph.D. (within the last 1-2 years) in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Statistics, or a related
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Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch, TensorFlow). Hands-on experience with game AI agents and/or GUI agents such as Mineflayer, Unity ML-Agents
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, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep Learning Video Understanding Multimodal AI Excellent programming skills in
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research infrastructure. Apply advanced statistical, machine learning and data engineering methodologies to large-scale, longitudinal datasets, contributing to innovative melanoma and skin cancer research
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develop statistical, mathematical, causal-inference, simulation, and machine-learning methods using large-scale flight trajectory and operational datasets. Research may cover airport surface congestion
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for: Conducting research and development on AI-based solutions for automated defect inspection and condition assessment of train components by designing and developing deep learning, computer vision, and machine
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of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical systems. Key Responsibilities Derive and analyse closed-form mathematical
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biophysics and machine learning for the accurate prediction and design of functional enzymes; and explore their real-world applications. Job Requirements Ph.D. in a relevant area, e.g., Chemical Engineering
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demonstrate how innovative methodologies can generate new knowledge within art history. Relevant topics may include, but are not limited to: Artificial Intelligence and Machine Learning in Art History Museums
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framework to find the optimal operation strategy Conduct computer programming to verify the efficiency of the designed solution algorithms Analyze data acquired from the field survey Develop machine learning