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processing, and sensor integration. Strong background in signal processing, computer vision, or machine learning. Proficient in programming languages such as Python, C++, and MATLAB. Strong publication record
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machine learning. Proficient in programming languages such as Python, C++, and MATLAB. Strong publication record in relevant research areas. Excellent communication and teamwork skills. Modes of Work Hybrid
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. Beneficial previous experience could include (but is not limited to): robotics and autonomous systems development, environmental science fieldwork, electronics design, and data science. Knowledge of Python, C
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and programming skills and experience of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience
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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | about 1 month ago
and Python. * “Courses not conferring an academic degree” must be related to the research fellowship’s activity type. “Courses not conferring an academic degree” are the courses referred to in paragraph
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encouraged to apply. A strong computational background, proficiency in R and at least one additional scripting language (e.g., Python) and knowledge in biostatistics are required. Experience working with
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, relevant experience in computer-based statistical analysis and presentation of results, demonstrated proficiency in a coding language used for data analysis, such as Python or R, strong quantitative skills
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, relevant experience in computer-based statistical analysis and presentation of results, demonstrated proficiency in a coding language used for data analysis, such as Python or R, strong quantitative skills
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management and programming languages such as R and Python. Level B Essential Criteria: A PhD in entomology or a related discipline, with at least 3 years of postdoctoral experience. A strong track record in
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studies, QTL studies, colocalization, Mendelian Randomization) Proficient in R, Python, Linux/Unix Command over commonly used genetics (e.g., PLINK, GCTA, FUMA, variant predictor tools) and metagenomics