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to the causal abstraction research direction, which aims to build rigorous benchmark for evaluating AI interpretability using the framework of causal abstraction and develop new interpretability methods
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build algorithms that make people and the world better. The full job description is here: https://www.dropbox.com/scl/fi/izvkooxmlzm44fe5u01ih/Job_Description_Predoc_Rambachan-and-Mullainathan.pdf?rlkey
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, algorithms, AI) in society. We are in particular looking for candidates who have interest and experience with STS and humanities pedagogy in the context of a technical university and in developing research and
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Qualifications: Deep knowledge about how biological neurons have been trained before, and new ideas on how to train them Prior knowledge and experience in encoding and decoding information from neurons Prior
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related to staff position within a Research Infrastructure? No Offer Description Job Title: Junior Researcher – Advanced ML & Computer Vision (m/f/d) (35h/week) Your Tasks / Responsibilities: Develop and
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developing and implementing bioinformatics pipelines and tools. Experience in sequencing data analysis (e.g., transcriptomics or proteomics), including emerging technologies (Visium, OPEN-ST, MIBI-TOF
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, correlation analysis) ⢠Integrate and benchmark generative-modeling approaches ⢠Design and implement XAI algorithms ⢠Collaborate with clinicians to validate interpretability outputs Software Development
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-doctoral Associate will develop algorithms and theory for machine learning methods, as well as implement and apply ML methods to problems in domains such as computational biology and neuroscience. This is a
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machine learning, statistics, or applied mathematics that could drive the frontier of biomedical research. The role will be focused on the development of novel computational and algorithmic methods, with a
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benchmark generative-modeling approaches • Design and implement XAI algorithms • Collaborate with clinicians to validate interpretability outputs Software Development & Prototyping • Contribute to a web-based