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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
management) Your Profile PhD in marine microbial ecology with a focus on molecular ecology or ecological genomics or closely related topics - ideally with a deep-ocean focus Professional experience as a
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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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communication) Willingness to learn and confront new challenges Preferred Qualifications Doctoral research conducted in the area of machine learning for healthcare and related topics Deep knowledge of multi-modal
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cutting-edge big/deep data analysis methods, including machine learning and artificial intelligence. The ideal candidate will therefore have a strong background in data science and in the application and
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profiles, to identify treatment response patterns, subtypes, and critical intervention windows that reduce Alzheimer's risk Disease (AD) risk. This position will involve applying machine learning, deep
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. Required Qualifications: Doctoral degree (PhD) conferred by start date Demonstrated experience with analysis of large health databases Training and experience in machine learning and deep learning methods
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. Are you interested in applying your machine learning and deep-learning expertise to develop cutting-edge ecological and environmental research? The Senckenberg Gesellschaft für Naturforschung invites you to
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frameworks such as GTSAM, G2O, or similar; computer vision frameworks like OpenCV; and/or deep learning frameworks such as PyTorch and TensorFlow Prior experience with industry or publicly funded research
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materials discovery, materials processing, and structural analyses. We also focus on educating engineering students at all levels, ranging from BSc, MSc, PhD to lifelong learning students. We have about 300
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computational framework, integrated with deep reinforcement learning (DRL) methodologies for both gene-level and edge-level perturbation control, represents a significant advancement in the computational toolkit