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Job Description Are you passionate about leveraging IoT, machine learning, and optimization to make energy districts and communities more sustainable? We are looking for a highly motivated and
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learning for dynamical systems, system identification and control systems. This can involve co-tutoring of PhD students or early R&D cooperative studies. contributing to the development of advanced benchmark
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and implement innovative image analysis methods to quantify plant characteristics. Collaborate on multidisciplinary projects involving high-throughput phenotyping platforms. Apply machine learning and
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the intersection of machine learning and genomics. The project involves the development and application of advanced machine learning and deep learning techniques to understand the sequence-function relationships
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developing and patenting new IP, with great impact for the fusion and materials communities. The successful candidate will join A/Prof Patrick Burr’s diverse and multicultural research group. To learn more
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development. The ideal candidate will have a PhD in a relevant biological subject, together with experience in molecular biology, cell biology and immunology. Familiarity with flow cytometry, vascular biology
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development. The ideal candidate will have a PhD in a relevant biological subject, together with experience in molecular biology, cell biology and immunology. Familiarity with flow cytometry, vascular biology
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in a manner that supports a research-led and scholarly approach to student learning and assessment in the discipline and/or profession. 3. Participate fully in examination and other assessment
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Employment 0.8 - 1.0 FTE Gross monthly salary € 4,728 - € 6,433 Required background PhD Organizational unit Faculty of Social Sciences Application deadline 17 August 2025 Apply now How and why did
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applicants regardless of their personal background. Qualifications and the selection process Applicants for this position must hold a PhD degree (or equivalent level of education) in bioinformatics, data