124 parallel-processing-bioinformatics Fellowship positions at Nanyang Technological University in Singapore
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, muti-modal, and multi-task processing/computing, for building open databases of climate data, monitoring and mitigating climate change, and predicting climate change and effects of mitigation Submission
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and process of coatings require understanding on the surface properties and mechanical properties. Basic understanding on electrochemistry and electrical properties will be required for the applications
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on PPIE projects in research and education, developing sustainable processes and infrastructure for PPIE, patient partner recruitment and relationship building, and developing capability building programmes
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experience in computer sciences Demonstrated experience in handling large size database Knowledgeable in theoretical physics, and at minima basic knowledge in theoretical plasma physics Demonstrated track
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are looking for a Research Fellow to support the research projects. The research fellows will work closely with the other institutes to integrate their systems into the processing of fibrous muscles. Key
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organizational and time management skills. Strong written and verbal communication skills. Proficiency in using software tools for NMR or cryo-EM data collection and processing is a plus. Familiarity with
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. Collaborate with a multidisciplinary team to ensure seamless operation of perception and control systems. Conduct experiments in real-world and simulated environments, with a focus on high-speed performance
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to laboratory operation for the project. Job Requirements: PhD degree in physics, mathematics, engineering or related field Strong background in in photonics as well as in the use of electron sources, such as DC
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undergraduate students. Provide logistical support pertaining to laboratory operation for the project. Job Requirements: PhD degree in physics, mathematics, engineering or related field Strong background in
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processing would be an advantage. Proficiency in statistical software (e.g., R, Python, SAS, or Stata). Experience with clinical informatics approaches (e.g., cluster analysis, machine learning, Bayesian