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-throughput experimentation is desirable. Proficiency in programming languages (Python/MATLAB) commonly used in machine learning applications is desirable but learning can be completed during the PhD. Excellent
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 2 months ago
-discipline to carry out research independently # Willingness to work on numerical and experimental tasks # Programming skills (e.g., Matlab, SIMULINK, Python) or other common process simulators # Excellent
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Assembly). Experience with the culture of mammalian cells (e.g. tumor cell lines, T cells). Experience in flow cytometry, cell sorting, or confocal microscopy Experience in Python/MATLAB, machine learning
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. Additional Qualifications: • Theory: Expertise in quantum optics, quantum information science, or quantum field theory. • Simulation: Proficiency in MATLAB or Python for simulation/modeling and data analysis
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. Experience in one or more scientific computing languages (Julia, Python, Matlab, etc) You must meet the requirements for admission to the faculty's Doctoral Programme (https://www.ntnu.edu/nv/phd) Good oral
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Proficiency in scientific coding and data analysis programming languages, such as Python or MATLAB, is a requirement. Experience with running snowpack and/or numerical climate models on for example High
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learning - deep neural network, recurrent neural network, LSTM Optimization theory Stochastic geometry Strong programming skills in at least one of the following: MATLAB, Python, JULIA or C++ Language
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with RNA-based assays, spatial transcriptomics, cryosectioning, or histology Basic computational or data analysis skills (e.g., R, Python, ImageJ/Fiji, MATLAB) Experience with plant care, genotyping, and
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interest in cytoskeletal dynamics and cancer biology Proficiency in Python and MATLAB for image and data analysis Experience with 3D/4D image processing or quantitative modeling Experience with optical flow
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is considered positively, but is not a requirement: live cell microscopy, image analysis, whole genome sequencing, physical/mathematical modeling, coding in Python and/or Matlab. Suggested reading