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phasing in humans and other species. In this project, we aim to develop machine learning models to advance the characterization of genetic variations. Research project 3. De novo genes are genes that arise
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with modern deep learning frameworks (PyTorch, JAX, or equivalent). Have
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complex or high-dimensional systems. Experience with physics-informed or constraint-based machine learning (e.g. neural ODEs, energy-based models) Experience with dynamical systems, stochastic processes
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Machine Learning group at TDB and SciLifeLab (Associate Professor Prashant Singh), which develops methods and software for simulation-based inference, generative models and robust machine learning, together
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agents Experience developing infrastructure for machine learning workflows Experience contributing to open data platforms or large scientific databases Awareness of diversity and equal opportunity issues
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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model organisms, their application in conifers remains limited due to challenges associated with tissue structure, nuclei isolation and sensitivity to inhibitory compounds. This project aims to develop
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platform to identify and optimize therapeutic candidates. Our group specializes in developing technologies to assess the multicellular environment within three-dimensional microtumor models. The project
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are essential to quality and form an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it’s like to work and grow at KTH. Trade union representatives