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Field
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of supervisors that covers both academic pursuits. Primary tools are the programming language Python to exploit its advanced deep learning toolboxes (e.g., Keras, TensorFlow, and PyTorch), alongside
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statistical analysis and/or coding (e.g., R, Python, C++) Exposure to neurophysiological measurement methods (e.g., eye-tracking, pupillometry) Interest or training in technology law, digital regulation, or AI
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in Python programming. Experience with machine learning methods, bioinformatics, and data science. Familiarity with generative AI tools for protein design and protein language models. Knowledge
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of the (computational) mechanics of solids and the finite element method and/or spectral solvers Practical experience in at least one programming language (preferably Python) and experience with the use of Unix/Linux
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(e.g. C++, Python, Matlab) OpenFOAM (beneficial, not required) Strong analytical and problem-solving skills. Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and
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and hands-on experience with AI and computer vision. Solid programming skills in Python, especially with PyTorch. Practical experience with deep learning projects, including working with attention
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biology, molecular biology including mammalian cell culture is required Documented knowledge in bioinformatics and programming (Python, R) is required Experience in LC-MS and mass spectrometry is required
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problems Proficiency in data analysis and programming using at least one statistical program such as R, Python, or similar programming languages Experience with GAMS, GTAP, and Exiobase is an asset. Skills
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with molecular biology, scRNAseq/ATACseq, in vivo 2-photon live imaging, and mouse behavior Experience with coding is an asset (e.g., R, Python, MATLAB). Ability to work independently and as part of a
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-footprint), emission models (e.g. IPCC, EMEP/EEA) and tools (e.g. Simapro); experience in programming languages (e.g. Python) to develop methods, models and tools is considered a strong plus. excellent spoken