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environments Change: Development of pMOS GaN Technology to complement nMOS GaN Impact: Operate microelectronics in harsh environments Job description We seek you as a PhD candidate to investigate enabling CMOS
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Delft and NXP Semiconductors as a PhD researcher developing next-generation testing solutions for analog and mixed-signal automotive chips. Job description The Computer Engineering (CE) section
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make extensive use of low-fidelity simulations which can provide fast but inaccurate solutions depending on the flow complexity. To close this gap, this PhD will explore machine-learning (ML) methods
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Science and Engineering at the Faculty of Mechanical Engineering, in close collaboration with the Netherlands Defence Academy. The CTE group is a dynamic team of researchers, including PhD candidates
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
to anticipate and manage. This PhD will develop a machine learning module to detect early warning signals of positive tipping points from techno-economic data, helping policymakers design adaptive strategies
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learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts. For example, we would like to be able
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asymptotic analysis of stochastic processes Impact: Faster detection of anomalies and reliable uncertainty quantification Job Description As a PhD candidate in Mathematical Statistics, you will develop novel
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climate resilient policies! Job description This 4-year fully funded PhD position is part of the ERC Consolidator project “Systemic physical climate risk in complex adaptive economies” (SPHINX). The SPHINX
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to support coordinated decision-making for sustainable strategies in the port call? As a PhD student at TU Delft, you will leverage AI (i.e. optimization and machine learning techniques) to prepare ports
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volatile geopolitics. Shortages, trade frictions, and financial mismatches can stall otherwise viable tipping dynamics and establish carbon-intensive lock-ins. This PhD will develop an agent-based inspired