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the foundations for future data-driven and AI-supported research in natural and engineering sciences. This is what you will do To fulfil the goals of the BridgeMD project, and the responsibilities of a postdoctoral
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collaborate with partners at ELLIS Institute Finland. Target publications at top-level AI, ML, and NLP venues. Who we are looking for Requirements Ph.D. in Machine Learning, Natural Language Processing
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developing optimization-driven approaches to multimodal device tailoring. We are looking for someone with A PhD in Human-Computer Interaction or a closely related field Strong programming skills (e.g., Python
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lab work includes the lab's recent Nature Genetics publication on proteome-wide Mendelian randomization and proteome-phenome mapping Primary Responsibilities The Postdoctoral Fellow will be expected
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scientific presentations internally and externally with exceptional oral communication skills. Interacts professionally with collaborators and fellow research team members. Working Conditions: This position
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, Raman, XAS, SAXS, etc) Good oral and written proficiency of English. Finnish language is not required High motivation and team player in interdisciplinary settings Knowledge in inorganic, metal-organic
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the theory of brain-inspired algorithms and apply them to complex, real-world problems. The successful candidate will join an interdisciplinary team of computer scientists, mathematicians, engineers, and
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protocols using computer-based data collection software, preparing and submitting ethics applications, drafting informed consent documents, and pre-registering studies. - Submit research protocols
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) Good oral and written proficiency of English. Finnish language is not required High motivation and team player in interdisciplinary settings Knowledge in inorganic, metal-organic, or organic chemistry is
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the integration of AI components transforms the nature of software systems (SE4AI). From an architectural perspective, the research investigates how the inclusion of AI elements—such as LLMs challenges conventional