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example through relevant work experience and/or peer-reviewed academic works. Experience in mechanical testing Experience with programming in Python or Matlab Strong analytical and problem-solving skills
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/phd ) Documented experience with Python or another relevant programming language, or with computational materials science/chemistry. Good oral and written presentation skills in English. PLEASE NOTE
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optimization. Strong programming skills (Python, Julia, Jax). Experience with numerical optimization is also desirable. Personal characteristics To complete a doctoral degree (PhD), it is important that you are
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experimental data acquisition system (LabView, etc.) and programming skills (Python, etc.) for data analysis would be a plus. Familiarity of cryogenics is not necessary. Intrinsic orientation for research and
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. Demonstrated strong proficiency in programming (e.g., Python, PyTorch/JAX, or similar) and computational skills are required for this position. Candidates must have excellent interpersonal and communication
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September 2026) in Computer Science, Artificial Intelligence, Applied Mathematics, Hydrology, Civil Engineering, Actuarial Science or a related field; Strong programming skills (Python; familiarity with deep
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the ability to analyse experimental data using Python, MATLAB, Origin, Excel, or similar tools. Good laboratory discipline, safety awareness, record keeping, teamwork, and communication skills. Entry-level
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, and data analysis. Knowledge of advanced statistical methods or data visualisation (e.g., developing Shiny web applications). Experience with Statistical Programming using R and/or Python. Experience
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plus Proficiency in at least one major programming language, such as Python, is expected Familiarity with deep learning frameworks and modern NLP toolkits is an advantage Motivation to publish research
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Science, Applied Mathematics or a closely related field. A strong mathematical background (probability, linear algebra, optimisation) and proficiency in programming (preferably Python/Matlab) are essential. Prior