259 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Netherlands
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machine learning/deep learning. You also have: a strong academic record and a PhD in bioinformatics, computational biology or related discipline proven proficiency in programming (Python) and machine
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brought forward striving academics, like the first female student, the first Dutch astronaut and various Nobel prize winners. We are seeking a candidate with a PhD in Bioinformatics, Machine learning and AI
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PhD* in a topic such as animal acoustics, machine learning, quantitative ecology, quantitative biology, signal processing or similar. Evidence of the ability to conduct high-quality research and write
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of manuscripts. Selection Criteria Strong independent abstract thinking skills, hypothesis driven bioinformatics A PhD in bioinformatics/molecular/cellular biology/ computational biology Strong bioinformatics
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cultural geography and demography. FSS is also well-embedded in a wider range of disciplines, such as social, political and technology sciences. Our vision is to spatially transform society to support
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composition, processing, and consumer responses. The AI system developed will utilize advanced techniques such as machine learning, natural language processing, and predictive modeling to detect potential
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Deadline 31 Mar 2025 - 21:59 (UTC) Type of Contract Temporary Job Status Not Applicable Hours Per Week 40.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the
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immunology to understand fundamentals of adaptive immune recognition, to design next-generation therapeutics and diagnostics. Model antibody-antigen interaction using machine learning. Develop and apply
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; Demonstrated interest in research on the intersection of society and AI, preferably as it relates to forms of algorithmic bias; Experience with machine learning or computational modeling; Strong quantitative
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programming skills in Python (experience with MATLAB or R is a plus). Proven experience with deep learning and machine learning frameworks (e.g., TensorFlow, PyTorch). Background in computational modeling