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that machine learning applications are developed with ethical considerations in mind. Participate in regular meetings with the research group. Required Qualifications* Ph.D. in Electrical Engineering, Computer
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scattering with machine-learning models trained on plant mutants, the project will shed new light on the cellular-level biochemistry that governs plant growth, development, and stress responses. This is a
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
, and safety profiles, and how these relationships can be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2. Modeling for Regulatory
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protein coding genetic association data with functional and machine learning-derived features 4. Developing methods to characterize the genetic architecture of autism Salary and Benefits This position is
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 15 days ago
conference presentations. You will be able to work both independently and as part of a diverse research team. Experience with machine learning, array seismology, or planetary mission data analysis (such as
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brain organoids. This position requires a high-degree of team work and interdisciplinary activities. What You Will Do: Perform research in machine learning methods based on control theory applied
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-C05-i08 of the Recovery and Resilience Program, through the Foundation for Science and Technology – FCT, under the following conditions: Scientific Area: Computer Engineering, Biomedical Engineering
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the field of the seismic behaviour of masonry structures and machine learning; Have a good proficiency of the English language. At the time of the respective hiring, candidates must prove that
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of programming and artificial intelligence.; - Knowledge of deep learning and computer vision.; - Autonomy. Minimum requirements: Strong knowledge of the English language (written and spoken). 5. EVALUATION
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Machine Learning model will be developed, capable of adjusting the electric assistance to optimise the balance between performance and consumption. Finally, the system will be validated with a real e-bike