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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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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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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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The successful applicant will hold a Ph.D. in agricultural economics or related fields. The successful applicant should have experience using statistical programming languages, such as R, Python, SAS, etc
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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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-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
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data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using programming languages like R, and/or Python. You have excellent