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. Preferred Qualifications Experience with: C/C++, Python, MATLAB, ROS 1 and 2, OpenCV, Unity, GPU programming, linear and nonlinear control theory, supervised, unsupervised and reinforcement learning, Torch
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such as Python, and experience with Linux cluster computing environment is strongly preferred. Candidates should hold a PhD or equivalent doctoral degree in biostatistics, human genetics, bioinformatics
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multidisciplinary team environment. Further, we will prefer candidates with some of the following qualifications: Solid background in programming using Python (PyTorch, TensorFlow), R or other languages. Experience
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multidisciplinary team environment. Further, we will prefer candidates with some of the following qualifications: Solid background in programming using Python (PyTorch, TensorFlow), R or other languages. Experience
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(e.g. fuel cells, batteries, heterogeneous catalysts, interfaces) Proficiency in high-level programming languages, e.g. Python Previous supervision of graduate students, as well as laboratory
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neural networks, knowledge graphs, and graph databases - Proficiency in programming languages such as Python, R - Excellent analytical and problem-solving skills - Strong communication and teamwork
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analytics - Proficiency in Python, deep learning frameworks (e.g., PyTorch, TensorFlow), and large-scale data analysis Additional Application Materials Required Cover letter CV Research Statement or Relevant
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the ability to train and manage student researchers and technicians. Preferred Qualifications Preferred experience: in vivo imaging, ex vivo electrophysiology, rodent behavior, Python/R/Matlab coding
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. Proficiency in statistical software (e.g., R, SPSS, Python, or MATLAB). A strong record of peer-reviewed publications. Strong communication, organization, and problem-solving skills. Preferred Qualifications
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. Experience with software development in a scientific environment using Python, Ruby, Julia, R, or C++. Experience programming in Python, Matlab or similar tools Strong interpersonal communication skills (oral