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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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to illness, parental leave, clinical service, trade union duties or similar circumstances. The PhD degree should be in a computational or life science field, such as bioinformatics, ecology, mathematics
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certificate of a completed PhD programme (required before the start of the contract) and contact details of 2 persons for reference, to be sent directly to olga.mula.hernandez@univie.ac.at referencing the job
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activities (if applicable), Bachelor and Master degree certificates, your certificate of a completed PhD programme (required before the start of the contract) and contact details of 2 persons for reference
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of PhD and MSc students, teaching and supporting in acquiring funds for future research projects from research funding agencies/councils, EU framework program or industry. Qualifications Eligibility
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mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
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can be found at the URL https://math.univ-cotedazur.fr/~ingremeau/PostdocNuHeMiBa.pdf We are looking for a candidate with a PhD in applied mathematics, with a strong focus on numerical analysis
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extended.) Person Specification Essential Criteria: Qualifications A good first degree in Mathematics, Physics or a related subject. A PhD (or be close to submission) in Applied Mathematics, Solar Physics
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. Qualifications PhD in Computer Science, Economics, Mathematics, or a closely related dis-cipline. Strong background in computational social choice, algorithmic game theory, or AI safety. Excellent research track
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base, the partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry scientists. Within the partnership, small research teams will