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Field
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: Education: Ph.D. in machine learning, computer science, engineering, physical science or related technical discipline. Experience: Expertise in developing and training AI models Proficiency in Python
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, engineering, physical science or related technical discipline. Experience: Expertise in developing and training AI models Proficiency in Python Experience with HPC (GPUs preferred) Related Skills and Other
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communication skills. Does this position have supervisory responsibilities? No Preferred Education/Experience Proficiency in bioinformatics and data analysis tools (e.g., R, Python, or similar platforms
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 26 days ago
she must have strong proficiency in Python and/or MATLAB, R or related software for data analysis and other software for statistical and mathematical modeling with evidence of codes in public
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culture techniques. Basic Immunological concepts. Preferred Qualifications Prior experience with live cell fluorescent imaging is helpful. Being proficient in Python, R, and/or command line applications is
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. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred: Dynamical Systems Formal
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fields : Computer Vision, Machine Learning, Pattern Recognition • Strong programming skills in Python • Good knowledge of Linux tools and environment • Autonomous and rigorous • Curiosity and creative
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. Strong quantitative skills (e.g., econometrics, optimization, I/O table, CGE, simulation modeling) and solid programming experience (e.g., Python/R/Matlab/C). Preferred (optional): familiarity with machine
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a strong background in statistical analysis and data-driven modeling applied to large astronomical datasets and time-domain surveys. Expertise in programming languages such as Python, R and C
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Python for machine learning and interpretability research. These tools will support systematic investigation of model behaviour and enable robust experimentation on large and complex datasets relevant