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Field
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). Develop robust, reproducible and reusable Python code for model training, inference, and large‑scale computational experiments. Run and manage high‑throughput workloads on HPC or cloud infrastructure
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‑on experience with Python or other scientific programming tools. Candidates must have a strong analytical mindset and willingness to work across experimental and computational domains, be proficient in oral and
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software engineering Solid Java/Javascript/Python programming skills. Solid mathematical background. Good background knowledge in the areas of business process modelling and model-driven engineering is a
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methods, strong publication potential, good command of English, programming skills in MATLAB, Python, R, Julia, or similar. Experience in one or more of the following is an advantage: time series analysis
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website http://www.secresy4you.eu . Where to apply Website https://www.tue.nl/en/working-at-tue/vacancy-overview/phd-on-secresy4you-hybrid… Requirements Research FieldEngineering » Control
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plasticity in recurrent networks - Publish research results in international journals - Present work at scientific conferences The researcher will join the Neural Theory Group at ENS, within a dynamic research
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://www.universiteitleiden.nl/en/staffmembers/laura-heitman#tab-1 at Leiden University! What you will do Project objectives are: Develop expression and purification methodologies for GPCRs Develop and optimize affinity selection
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/Qualifications Deberá acreditar conocimientos en: Programación en C/C++ en plataformas Linux y Windows, Python, Java, JavaScript. Configuración de proyectos con CMake. Conocimientos de boost, QT, Typescript
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and programming skills, namely in the use of MATLAB and Python. Candidates who do not meet these requirements will be excluded. Additional optional skills and qualifications: Proven experience in
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related field Solid understanding of machine learning, especially deep learning and transformer models Practical experience with Python and ML frameworks (e.g., PyTorch, HuggingFace, NumPy, sklearn) Basic