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Field
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atomistic simulations. This project requires creative thinking and programming, as well as technical expertise in materials simulations, electron–phonon physics, and machine learning. We further appreciate
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project is to use discrete optimisation techniques (e.g. Integer Programming) to design new algorithms for MAPF. An example of previous research in this direction was published in IJCAI. Required knowledge
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programming background with fundamentals in machine learning and data science. Experience in building visualisations and interactive immersive environments (using game engines like Unity3D) is
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. The successful candidate will have Proven knowledge of soil science, fire ecology, and reed ecology Proven knowledge of data analysis and programming (R applications or comparable programming languages
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in data analysis and programming (R-applications or comparable programming languages) using large datasets Determination and commitment to scientific excellence Willingness and ability to drive to
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machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch or TensorFlow) Project funding Other Funding
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. A strong background in energy-system modelling, mathematical optimization, machine learning, reinforcement learning, or data-driven decision-making is appreciated. Experience with programming and
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programming languages and frameworks such as Python, PyTorch, or TensorFlow is an advantage Fulfillment of the formal admission requirements for doctoral studies at the University of St. Gallen
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or life-science discipline. Strong programming skills and experience with large-scale sequencing, genomics or comparable biological datasets are required. Experience with ribosome profiling, machine
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. Basic Qualifications Ph.D. or equivalent terminal degree in quantitative, computational, or biomedical discipline. Programming proficiency in Python, Julia, R, or MATLAB ; background in mechanistic