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Field
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candidate will conduct advanced methodological and psychometric research. Potential topics include (a) AI, machine learning, and large language models for measurement challenges (e.g., for small-sample
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quantitative analyses or master game theoretic analysis. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. Alongside
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research targeting a broad range of areas within NLP, including training and benchmarking of large language models (LLMs). The research profile of the group is heavily machine-learning oriented and the group
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in foundational neural models that learn from large unlabeled image datasets, also incorporating context from additional data such as wireline logs or well reports. You are suited for this position
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close connection with experimental data from live imaging and spatially resolved gene expression profiling. The work of the PhD fellow will be theoretical and computational in nature and will include
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AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
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candidate. The overarching theme will be the interplay of training data composition (e.g. different types and selections of data) and fine-grained evaluation in the development of large language models. LTG
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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equations, stochastic partial differential equations, stochastic mean-field equations, stochastic control and filtering, stochastics for data analysis and machine learning. These areas will be prioritized
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collaborators. Mentor junior colleagues and students. Write, present, and publish research findings in peer-reviewed journals. Knowledge and Experience Requirements: PhD degree in statistics, computer sciences