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; machine learning or deep learning; pharmacogenomics; computational biology; drug discovery; network medicine; NLP/large language models; or precision medicine 2. Strong programming skills in Python, R
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be advantageous: Experience with scientific expeditions Experience in computer-aided analysis of biological sequence datasets (e.g., with R, Python and Bash/Linux environments) Basic understanding
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(meta-)genomes Experience in the computer-assisted analysis of large biological datasets (e.g., using R, Python, and Bash/Linux environments) Very good written and spoken English skills Ability to work
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, or Python. The studentship covers fees at Home rate (UK and EU applicants with pre-settled/settled status and meet the residency criteria). International applicants must cover the difference between Home and
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for admission to a PhD programme. Strong background in Computational Mechanics, Solid Mechanics and Numerical Methods. Knowledge of the Finite Element Method. Programming skills (Python & Fortran). Good written
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structural dynamics, hydrodynamics, or marine systems Experience with programming or numerical tools (MATLAB, Python) A willingness to work across disciplinary boundaries Motivation to tackle open-ended, real
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experimental data. • Proficiency in scientific programming and data analysis tools (e.g., Python, R, Linux/Unix environments). • Demonstrated track record of publishing scientific results in peer-reviewed
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related field with a strong quantitative focus. Strong programming skills in Python and demonstrated experience with machine deep learning frameworks (for instance, PyTorch or TensorFlow), preferably
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(R, Python, MATLAB, or similar). Willingness to conduct fieldwork in Arctic conditions. Proficient in both written and oral English. You will be offered the opportunity to participate in digital
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 29 days ago
: Good programming skills, preferably in Python, and strong analytical abilities. A solid background in machine learning, computer security, privacy, distributed systems, or a closely related area