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in machine learning and artificial intelligence Experience with numerical analysis and scientific computing Knowledge of power systems and renewable energy technologies Experience in power system
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fees will be covered for up to three years. Eligibility Applicants must have an undergraduate or postgraduate qualification in computer science, machine learning, AI or another relevant field. Equivalent
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. Preferred qualifications: D. in Quantitative Genetics/Genomics, Computational Biology, or Related Discipline. Skilled in single-cell transcriptomic analyses, machine learning and artificial intelligence
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Abandonment (P&A) Technology and within the framework of the ongoing industry sponsored research program SFI – Center for Subsurface Well Integrity, Plugging and Abandonment (SWIPA) https://www.sintef.no/en
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identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law or policy. Qualifications Please reference
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publications in peer-reviewed journals and presentation at consortia meetings, national and international conferences, and workshops Your profile Essential qualifications: MSc degree in materials science
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grades in the field of mechanical engineering, material science, physics, computational science or similar, preferably with a specialization in the field of theory and/or simulation Strong understanding
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degree (or equivalent) and/or a relevant postgraduate masters’ qualification (MSc) in materials science, engineering, mechanical Engineering, chemical Engineering, physics, or a related discipline. (E