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Field
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of programming, computer vision, machine learning, or related discipline. Experience with Python and relevant AI/ML libraries (such as PyTorch, TensorFlow). Demonstrated knowledge of multimodal data processing
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with primary human tissues and cells, particularly liver tissue, is an advantage. Experience with library preparation for single-cell and/or single-nucleus RNA sequencing (scRNA-seq/snRNA-seq) is an
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and high-resolution microscopy. Hands-on experience working with primary human tissues and cells, particularly liver tissue, is an advantage. Experience with library preparation for single-cell and/or
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FDA-approved drug library repurposing and fragment-based drug discovery (FBDD) to search for potential modulators. As part of the POLARIS doctoral network, you will develop a functional GPCR assay
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, diagnostic tracers and/or therapeutics. You will generate and characterise nanobodies using immune and synthetic nanobody libraries as well as AI-generated candidates. The project will involve in vitro and in
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Opens external , FAME or QuEUR. - Time and space for your development in the broadest sense: development days and a personal career budget and free access to our university library
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The International Institute of Molecular Mechanisms and Machines Polish Academy of Sciences | Poland | 26 days ago
is a slow, expensive, and labor-intensive process. Large sums are invested into screening compound libraries to identify initial hits and structurally optimizing these initial compounds for human
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. Knowledge of data analysis, optimization, and machine learning techniques is a plus. Knowledge of machine learning libraries (e.g., PyTorch or TensorFlow), SDR hardware (e.g., USRP) and software (e.g
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technologies in NLP, LLMs, and RAG. Programming skills in Python, including experience with machine learning libraries (e.g., PyTorch, Scikit-learn, Hugging Face Transformers) and development tools (e.g., Co
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technologies in NLP, LLMs, and RAG. Programming skills in Python, including experience with machine learning libraries (e.g., PyTorch, Scikit-learn, Hugging Face Transformers) and development tools (e.g., Co