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capabilities to aid in the predictive engineering of biological systems, such as proteins, as part of the NIST Engineering Biology Program. Develop artificial intelligence and machine learning analysis pipelines
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members of the group help each other. Opportunity for growth as a software developer in areas such as CUDA programming, analysis of neural data, machine learning model applications, and real-time
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environments, and assessing model explainability. You'll work closely with a team of graduate students, postdocs, and other collaborators to develop innovative AI models, create software tools, and establish
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and machine learning. Internal further training & coaching: The Vienna Doctoral School as well as the Department of Human Resources offer plenty of opportunities to grow your skills in over 600 courses
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Quantum Fundamentals, ARchitectures and Machines program (Q-FARM) is an interdisciplinary initiative woven throughout the university. Q-FARM harnesses the expertise and facilities of Stanford University and
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with experience in ligand discovery. Our research group is focused on developing state-of-the-art computational methods for ligand/drug discovery, using machine learning, high-performance/cloud computing
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and early-onset cases without a known genetic cause. We are also interested in genetic interactions (epistasis), tandem repeats, machine learning, and other areas of AD research that have not yet been
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40 staff members and 50 postdocs and PhD students. This position will also be affiliated with the AI4Science Lab , which originated out of the Amsterdam Machine Learning Lab (AMLab) at the Informatics
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tools (e.g., FSL, FreeSurfer, AFNI, ANTs, fMRIPrep, QSIPrep) into standardized processing streams. Support advanced modeling approaches including network analysis, multivariate methods, machine learning
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the interplay between mutations, energetics, and evolutionary constraints, including epistatic effects. · Developing or applying machine learning approaches to predict or redesign frustration patterns in proteins