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Field
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particle physics phenomenology -programming in C++ and/or Python -fluent knowledge of English language Welcome: experience in Monte Carlo methods and statistical modeling experience in QCD phenomenology
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candidate is expected to hold: A master degree in biomedical engineering or computer science, Excellent programming skills (Python). Experience with data curation, large-scale datasets, and machine learning
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, material processing, and/or atomistic simulations is an advantage. Excellent analytical skills and proficiency in scientific programming (e.g. in python) are an advantage. Excellent oral and written
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packages and tools (e.g., Numpy, Pytorch, Tensorflow, ART). You have knowledge or familiarity with reverse engineering tools (e.g. NSA Ghidra, IDA Pro) You have experience with Python, C/C++, or low-level
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software as python packages Report findings and methods in conference and journal papers Your profile Masters, Diploma or equivalent degree in IT/computer science/statistics/applied mathematics/data science
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. Programming experience (e.g. Python, MATLAB, C/C++). Desirable (but not required): Background in control theory, dynamical systems, optimisation, or machine learning. Experience with robotics, ROS, simulation
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use other programmes, such as Python, Stata, or SAS. Application deadline Please check the HDS PhD website in autumn 2025 for updated information on 2026 HDS PhD programme admissions. The application
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or data-analysis skills (e.g. Python, MATLAB) experience in collaborative or international research environments We offer you an exciting and interdisciplinary research environment within the BlueMat
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research Experience in cell culture and molecular biology Programming or data analysis skills such as R, Python, or similar Desirable skills: Interest in neurodegeneration, RNA biology, or phase separation
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modelling, including experience in using a programming language suitable for geospatial data analysis (e.g., R, Python). Experience in applying remote sensing methods, for instance in ecological, geophysical