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Are you passionate about using data science and machine learning to address mental health inequalities in rural and coastal communities? The University of Lincoln is seeking an ambitious
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have a PhD in physics, biology, or a related field by the time of appointment. The ideal candidate will also have demonstrated experience in machine learning and biological data analysis and a strong
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energy consumption in information processing and machine learning (e.g., arXiv:2308.15905); Quantum phenomena in information processing: exploring how quantum effects can be utilized to process information
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data management and machine learning is also preferred. An interest in energy system topics such as the green transition, sustainable energy systems, digital energetics etc. is preferred. Experience
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/DPhil in robotics, computer science, machine learning, informatics, AI, or a closely related field. You will have an excellent academic track record in topics relevant to locomotion and manipulation; path
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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | 5 days ago
, scikit-image, SimpleITK, etc.), will be given special consideration. Knowledge of machine learning and artificial intelligence techniques is a plus. Previous experience in code parallelization, the use
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processing, machine learning, and optimization theory. Strong verbal and written skills in English. Excellent analytical and problem-solving skills, and capacity to pursue independent research as
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analyses. Machine learning for biological data (e.g., protein language models, transformers, generative models) and interest in building interpretable tools for experimental colleagues. Qualifications PhD
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or electrochemical system PhD in Chemistry/Materials Science/Physics Encourage initiating activities on MOF development, devising, and analytical process Experience in machine learning will be preferred Good oral and
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modelling, or machine learning). Proficiency in statistical programming (e.g., R, Python, Stan, or similar). Experience developing and managing longitudinal studies. Evidence of contributions towards