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Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences. Embracing
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Requisition Id 16254 Overview: Do you have a passion for applying AI methods for accelerating scientific discoveries and an ability to think outside of the box in a collaborative and open
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. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab
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interdisciplinary collaboration. Proficiency in programming is required. Excellent knowledge of oral and written English is a requirement. You are expected to be able to teach in English. Additional qualifications As
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collaborate closely with experimental partners (ICCF, IJL, IC2MP, and Syensqo) to validate computational predictions, ensuring the development of catalysts that are both highly active and stable under harsh
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observational studies using the James Webb Space Telescope (JWST). The successful candidate will work with Prof. Daniel Eisenstein and collaborators on the analysis and interpretation of JWST data, with
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. The appointees will participate in a multidisciplinary collaborative research project related to development of deep learning model for diagnosis and prognosis of different sarcomas. He/she will develop and train
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neural population dynamics recorded by experimental partners - Collaborate with project partners - Participate in scientific activities of the team and scientific consortium - Study learning mechanisms and
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. This position is part of the DOE-BES initiative Integrated Scientific Agentic AI for Catalysis (ISAAC), a multi-facility collaboration integrating experimental measurements, simulations, and data science to
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position in machine learning (ML). This position offers a unique opportunity to conduct both basic and applied research in concert with collaborators working on diverse scientific and security problems