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development, data management, and grant reporting Profile PhD in microbiology, biophysics, bioengineering, or a related field (completed or near completion) with at least a first author publication
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clean energy transition at the individual and regional level (drawing on labor market surveys and LLM), (2) to collect original survey data on individual-level policy preferences in response to the
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machine learning to join an interdisciplinary project at the interface of single-cell genomics, data integration, and developmental biology. The project focuses on the integration and analysis of multi
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laboratory experiments, measurements in real-world buildings, data analysis, and modeling. Candidates with complementary expertise who are interested in developing new research directions within the
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Foundation model architecture development: novel pretraining objectives, tokenization, and scalable architectures for biological data. Multimodal learning: representation learning across diverse biological
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and economic relevance. You will plan and conduct experimental research, including experimental campaigns, data analysis and process optimization, while ensuring high standards of laboratory practice
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, tokenization, and scalable architectures that integrate diverse biological data modalities. Multimodal representation learning: aligning and fusing information across modalities to capture biological systems
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advanced computational modeling, mathematical modeling, or experimental psychology. Profile We thrive on interdisciplinary collaboration. You should hold a PhD in musicology, computer science, cognitive
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background who are excited to advance the state of the art in generative AI. Prior experience with biological or biomedical data is not required. The position comes with dedicated access to the Swiss
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their neuronal and physical microenvironment. Qualifications We welcome applicants with a PhD in neuroscience, cancer biology, cell biology, molecular biology, bioengineering, or a related field, together