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Familiarity with brain imaging analysis acquisition Knowledge of multivariate statistics and/or digital signal processing Proficiency in Python. Parallel computing processing experience is highly appreciated
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. Required qualifications: PhD in a field such as physics, systems biology, applied mathematics, machine learning, or related fields. Strong programming skills (e.g. Python) and experience with modern ML
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experience and track record in using open databases and bioinformatic tools and services. Strong programming skills for developing computational tools or data workflows (Python or similar), with good software
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., Python, Fortran, C++) Knowledge of data analysis techniques and statistical methods Proven scientific writing and oral communication skills Ability to work both independently and collaboratively in a team
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analysis. Programming experience (e.g., R, Python, or related languages) is preferred but not required. Applicants should also be willing to mentor undergraduate and graduate students, as the research
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Python Statistics Data analysis Communication (written and verbal) This does not mean you need expertise in these areas but rather that you have some base knowledge upon which you can build expertise
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coding, particularly Python, and utilization and management of codebases such as Github. Proven ability to work independently and as part of a team. Excellent communication, writing, and presentation
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programming, e.g., in Python, PyTorch, TensorFlow, or similar. Curiosity and willingness to work with researchers from a heterogeneous group, with core expertise in communication theory, networking, information
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programming in Python and the modern AI ecosystem, including PyTorch, HuggingFace, and ONNX and you are comfortable working systematically with complex technical problems. Experience with industrial
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certificate, IELTS or TOEFL). Programming skills (e.g., Python, C++, Rust). Interest in collaborative research with both academia and industry. Qualification requirements The eligibility criteria