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sufficient knowledge in the discipline and of research methods and techniques to work within established research programmes. Applicants will have coding skills in Python, PIP, JSON and HTML and will be
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of working with quantitative data and relevant software (Stata, R, Python, or similar). This is a full-time, fixed-term post for 12 months, that must end no later than February 2027. The Department of Social
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, or Python. The ideal candidate will demonstrate overlapping interests with Peter van der Windt’s research, which more recently includes work on conflict displacement. Given that much of Van der Windt’s work
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, biostatistics, biomedical informatics or related fields OR a PhD degree in biology or related fields. Preferred Qualifications: A strong background in programming (e.g., Python or R or C++) is essential. Research
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models and reconstruction algorithms. Develop or adapt scripts (e.g. in MATLAB, Python) to facilitate data analysis workflows. Prepare scientific reports, publications and presentations to disseminate
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at time of start) PhD or equivalent international degree. Preferred qualifications PhD in physics or astrophysics field. Strong programming skills (e.g., Python) for scientific computing and simulation
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programming languages such as Python, Go etc. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Interpersonal skills, oral and written communication skills, and ability
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Information Eligibility criteria * Skills/knowledge: statistical physics, physical chemistry, machine learning * Expertise: molecular simulations, programming experience (Python, C++, Julia, C, etc.), Python
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solid experience in programming, particularly in Python and JavaScript. Significant experience in data science and machine learning will be highly valued. You like to work in a team while demonstrating
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date). Strong background in power systems analysis (OPF, state estimation) and numerical optimization/control. Proficiency with Python/MATLAB and power-system toolchains (e.g., MATPOWER/OpenDSS