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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
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Section, Department of Civil and Mechanical Engineering, Technical University of Denmark (DTU). We look for a talented, self-driven, and collaborative individual with a passion for tackling complex
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public datasets as well as samples generated through collaborations. Ideal candidates will have strong interests in microbiome-host biology, bioinformatics, or machine learning. Experience with R or Python
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collaborative efforts among researchers at the University of Utah and UC San Diego in developing and applying methods in predictive and causal modeling of complex biomedical and social processes and systems
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that demand interdisciplinary solutions? Then the Program for Collaborative Doctoral Projects is the perfect opportunity for you. Many of today’s most pressing problems can only be tackled through
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reinforcement learning problems. We are looking for a profile with the motivation and drive needed for making a difference that matters. You must bring an open mindset and like to create results via collaboration
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be done in close collaboration with the other members of the FADOS network. Further information on the project is available at: www.fz-juelich.de/en/ibi/ibi-3/organization/neuroelectronic-interfaces
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HPC environments Good communication skills to interact with collaborators ranging from machine learning researchers to pathologists or medical students Knowledge of biology and medicine is a plus Highly