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storage, but their widespread deployment is limited by challenges in energy density, stability, solubility, and cost of electroactive redox compounds. The PhD candidate will develop and apply machine
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conducting research "in the wild" (e.g., field deployments or data collection in real-world environments) Familiarity with current AI technologies (e.g., machine learning, large language models) and an
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candidate will have a PhD in a relevant biological subject, or MSc / MPhil plus significant experience in industry in the field of computer vision and machine learning. They will also have extensive research
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datasets with machine learning methods, and software development are beneficial Good organisational skills and ability to work systematically, independently and collaboratively Effective communication skills
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networking and computer security, and genuine interest in the PhD project. We value a collaborative attitude and an interest in working both in teams and independently. Self-motivation, attention to detail
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the researchers from Department of Automation and Process Engineering will play a key role. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early
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” research themes. The successful candidate will have: a PhD in Translation Studies/Machine Translation; practical experience conducting data-driven research in a machine translation/large language models (LLM
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for their stakeholders and society at large through our MBA, MS, PhD, and Executive Education programs. We are equally committed to cultivating new scholars and teachers and to creating and disseminating pathbreaking
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collaborate with experts in computational mechanics and machine learning. How to apply ?: Please submit a detailed CV, at least 2 recommendation letters or contact information of people who can recommend you, a
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pipeline for learning from a large-scale microscopy dataset. You will work with expert computational scientists, data engineers, and experimentalists to train models that learn foundational embeddings