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Field
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new applications of machine learning and artificial intelligence to experimental data analysis. Make advances in one or more of: multimodal data fusion/joint inference; uncertainty quantification with
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expected, along with familiarity with FEA tools such as ANSYS and CAD software such as Creo and Fusion 360. Candidates should demonstrate strong research potential or an established publication record, with
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Proven experience with deep learning frameworks such as PyTorch, and familiarity with multimodal data fusion is highly desirable Ability to work independently, as well as collaboratively in
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fusion is highly desirable Ability to work independently, as well as collaboratively in interdisciplinary and international research teams Willingness to supervise junior researchers and develop your
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this position will also possess: Demonstrated experience developing or applying wet-chemical sample preparation methods (e.g., alkaline fusion with NaOH or LiBO₂, HF/HNO₃ digestion, MAGIC/Mg(OH)₂ pre
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robotics, controls, autonomous systems, sensor fusion, mapping, or a related area. Excellent written and oral communication skills. Motivated self-starter with the ability to work independently and to
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and AI approaches. Integration of proteomics with genetic data via MR, coloc and FUSION to identify causal and druggable targets. Working Conditions: This position will be located at Dr. Cruchagaâ™s lab
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methods for approaches such as model-data fusion techniques. The main focus of your position will be to develop and apply a scalable process-based modeling framework to evaluate climate-smart management
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Intelligent Control Systems RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis
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, Fusion, VERICUT, or similar platforms. Strong record of scholarly output, including peer-reviewed publications, conference presentations, technical reports, or open-source research tools. Excellent written