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Field
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. Applicants should have strong expertise in computational analysis of electrophysiological data as well as proficiency in large language models and machine learning algorithms. First-hand experience in
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of algorithms and models to realistically simulate forest ecosystem dynamics under varying conditions of land use change, forest and land management, climate variability, and other environmental stressors
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algorithms. Strong programming skills in Python and/or R. Experience in analyzing both quantitative and qualitative research data. Experience in statistical consulting, dashboard development, and data
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openness to interdisciplinary collaborations Expertise in some area of computer science such as computational complexity, algorithms, data structures, logic in computer science and AI, semantics, theory
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interplanetary travel. Autonomous Systems: Develop AI algorithms, robotics, and autonomous software to enhance spacecraft operations and exploration. Implement machine learning techniques for improved autonomy and
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exploration. Data Science and Analysis: Develop algorithms and tools for data processing, modeling, simulations, and analytics to support scientific research and mission planning. Spacecraft Operations: Oversee
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for aircraft and spacecraft. Develop and enhance avionics components and systems, including integrating avionics with guidance, navigation, and control (GN&C) algorithms. Focus on advancing autonomous flight
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the appointment, and will have experience in asymptotic, algorithmic or probabilistic combinatorics, or a closely-related area. Applicants should also be active researchers with good written and oral
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(Required) Experience creating and utilizing statistical algorithms and conducting power calculations to determine sample size to answer complex research questions. (Required) Demonstrated experience working
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assist in providing customers with state-of-the-art visualization and decision support. The primary focus will be researching and developing feasible algorithmic solutions to our sponsor’s complex problems