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1st August 2026 Languages English English English The Department of Energy and Process Engineering has a vacancy for a PhD Candidate in Plastics Pollution Modelling and Life Cycle Assessment Apply
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mechanisms, Sensors and instrumentation, Data analytics, machine learning, or AI, Digital twins or physics-based modeling, wide-bandgap semiconductor technologies. Excellent written and spoken English
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Application dates Applications close30 September 2026 What you'll receive You'll receive a stipend of $41,555 per annum for a maximum duration of 3.5 years while undertaking a QUT PhD (1.75 years
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microfluidic fabrication and experiments 3D printing machine learning. Demonstrated programming skills (Matlab, C++, or Python). Desired Demonstrated ability to work independently and to formulate and tackle
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research experience in e-health, digital health or a related field experience of, or a documented interest in, machine learning, AI methods or large language models (LLMs) in clinical or health-related
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alloys for energy applications in harsh environments using additive manufacturing. This research involves integrating computational modeling, machine learning, and experimental investigations to design and
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research excellence and the ability to pursue a doctoral degree at TUM Documented experience in machine learning and/or natural language processing with deep knowledge of LLMs, RAG and agent-based AI systems
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scientific results, talented PhD students need to acquire knowledge and are also required to exchange knowledge and experience with other PhD students in method-oriented working groups. Supervision by two
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: Computer science » Computer systems Computer science » Informatics Engineering » Computer engineering Engineering » Electrical engineering Researcher Profile: Leading Researcher (R4) Established Researcher (R3
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at Swinburne Early Entry Program University entry requirements Transferring to Swinburne Recognition of prior learning in the workplace Study abroad in Melbourne Opportunities for Indigenous future students