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presentations and draft sections of research papers Location: Department of Economics, Harvard University, Cambridge, MA Supervisor: Professor Gita Gopinath, website: https://gopinath.scholars.harvard.edu
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. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning is desirable but full training will be provided. Interviews for this studentship
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in R and/or Python Excellent communication skills and fluency in English (oral and written) Independent, proactive, rigorous, results-driven, solution-oriented, and a critical thinker Collaborative
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) and immunoassays (ELISA); Bacterial culture and transformation; molecular cloning; Experience with immunocytochemical techniques and fluorescence microscopy; Programming in Python; Independence and
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in written and oral English. Programming skills with Python. Consideration will also be given to good collaborative skills, drive and independence, and how the applicant’s experience and skills
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mechanics, or advanced material behavior. Programming skills (e.g., Python, MATLAB, Julia,..), ideally with experience in numerical methods or scientific computing. Familiarity with machine learning
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engineering Very strong mathematical and algorithmic background Programming experience (Python, C++, etc.) Familiarity with parallel programming frameworks (e.g. MPI, CUDA) Fluent in written and spoken English
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, Computer Science, Neuroscience, Data Science, or a related field. An MSc degree in a relevant area is desirable though not necessary. Experience in coding (e.g., Python/R/Matlab) and experience in
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, materials science and/or process engineering systems is an advantage Knowledge of relevant programming languages for data processing/evaluation, especially Python/ Matplotlib, is an advantage Independent
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Proficiency in at least one programming language (Python, C++, …) Keen interest in neuroscience is essential Experience with modelling, analysis of complex dynamical systems, simulation, analysis of large-scale