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develop advanced models, algorithms, and control solutions for simulating, optimizing, and operating future integrated energy systems. We address the challenges arising from the increasing integration
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computational biologist to collaborate with a multidisciplinary team to develop innovative computational algorithms and approaches to address crucial questions in cancer biology. The role will involve
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of trustworthy neurosymbolic AI. There is both a theoretic and practical component to this topic, since the ultimate goal is to implement efficient algorithms that allow reasoning engines to produce
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dynamics out-of-equilibrium, and applications to quantum simulation, quantum optimisation, and related quantum algorithms. Depending on the candidate’s interests and expertise, the project may also involve
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implementing bioinformatics algorithms, your work will directly shape how biological questions are translated into computational analyses, enabling researchers to unravel complex traits and predict phenotypic
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new algorithms for focused research questions in bulk and single cell tumor analysis, integrate tools and databases into existing high-throughput pipelines, and drive projects to completion and
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of specialty. Proven success collaborating across disciplines. Demonstrated ability to lead projects and guide team members. Recognized expertise with algorithms, pipelines, or systems development. Desired
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closely connected with quantum technologies and physical platforms. Relevant areas include but are not limited to: quantum information and computation; quantum algorithms and complexity; quantum error
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reduced teaching responsibilities. We are a federal competence center for AI education and research. Our research pushes the boundaries in numerous aspects of intelligence, including algorithms and models
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programming, microfluidics, and engineering to design sophisticated algorithms for exploring and manipulating information encoded in DNA-based graphs. The position is fully funded by a prestigious CNRS