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considered until the position is filled. To be considered, all applicants must submit: Cover letter Curriculum vitae with complete publication list BSc, MSc and PhD transcripts Research statement elaborating
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experiences and individual differences, as well as cognitive modeling of decision-making in both lab and realworld settings. Successful candidates will be supported in building a research program at the
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or military collaborators. You are expected to have strong mathematical and programming skills, knowledge of computer vision and data mining tools, and be able to work on group software projects using modern
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position is embedded in a vibrant research environment that includes several PhD students and postdoctoral researchers. The project is a close collaboration between the Computer Vision Group at Chalmers
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defence and security, working directly with industry and sometimes governmental or military collaborators. You are expected to have strong mathematical and programming skills, knowledge of computer vision
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vision research. The department fosters interdisciplinary collaboration, addressing real-world challenges through innovative machine learning, data science, and intelligent systems research. About the role
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Pension Eligibility ABP Qualifications Minimum Education and Experience PhD, MD or MD/PhD in psychology, cognitive neuroscience, computational psychiatry, or a related field. Proficiency with programming
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assistance for completing a degree while a Texas A&M employee Living Well, a program at Texas A&M that has been built by employees, for employees All positions are security-sensitive. Applicants are subject
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-of-the-art in SLAM, situational awareness, computer vision, machine learning, robotics, and related fields Developing and implementing innovative solutions, validated through real datasets and experiments
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 19 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different