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computational modeling and/or analysis of complex biological systems, integrating state of the art tools such as machine and deep learning approaches. Experience in managing biological databases and statistical
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developing a machine learning (ML) algorithm for the automated analysis of the above-mentioned mass spectra. Desirable: - knowledge in the field of Planetary Sciences - very good written and spoken English (C1
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at industry-facing events. Strong technical and scientific knowledge in machine learning, preferably with experience in large language models (LLMs). Solid foundations in mathematics and engineering
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an outstanding track record or strong interest in the fields of antibody design, structural biology, AI/machine learning, or immuno-oncology, we invite you to apply with a project proposal for a position in our
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that integrate machine learning with energy system optimization to make such policy optimization possible. The analysis will include policies for EV adoption, EV charging infrastructure, and electricity pricing
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, lubrication and wetting, biophysics as well as machine learning or metamaterials. For experimentalists, we particularly interested in applicants aligned with our existing research infrastructure and
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criteria PhD in Biomedical Engineering or Computing – awarded or near completion* Display excellent knowledge of cortical neuroanatomy Programming experience in Python Experience with machine learning
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including power and energy systems, wireless and photonics systems, semiconductors, RF, and devices, AI and machine learning, advanced computing, cyber-physical systems, and bioengineering. The department is
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research approaches: health and wellness promotion, a strong nursing workforce, lifespan health, AI/machine‑learning and informatics, population/global‑planetary health, and a just healthcare system
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About MINDS Centre for Doctoral Training The Machine Intelligence for Nano-Electronic Devices and Systems (MINDS), is a very successful £5M Centre for Doctoral Training (CDT) funding around 50 PhD