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addition to teaching duties, the PhD candidate is expected to conduct research in the field of (deep) machine learning, with applications in either biomedical image understanding (e.g., surgical video analysis in
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learn to apply these processes judiciously while maintaining a focus on originality and professional integrity. Candidates should have a relevant Master’s and/or PhD in Digital Arts, Computational Media
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‑on experience with common machine learning / deep learning frameworks (eg. PyTorch or JAX) applied to biological or structural data. Solid Python programming skills, with experience building maintainable and
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demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning
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Researcher (R1) Positions PhD Positions Application Deadline 31 Jul 2026 - 14:01 (Africa/Abidjan) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 36 Offer Starting Date 2 Nov 2026
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within SCI and across other departments within Pitt, and initiatives like the $11.6M Western Pennsylvania Quantum Information Core (https://www.pitt.edu/pittwire/features-articles/pitt-investment-pa
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, KELIM score), mutational profiles, histopathological information, and long-term survival outcomes. The first objective is to implement automated deep-learning–based segmentation of primary ovarian tumors
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Engineering in the 2025 QS World University Rankings by Subjects. The EEE Rapid-Rich Object SEarch (ROSE) Lab focuses on research in: (i) visual search & retrieval, (ii) video analytics & deep learning, and
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solutions that enhance ecological monitoring, improve resilience planning, and promote sustainable resource management. Development of a Detection Transformer through Attentive Deep Learning and Explainable
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) for science with Dr. Aleksandra Ciprijanovic (alexciprijanovic.com) and her research group! The successful candidate will join a multidisciplinary team working at the intersection of deep learning, cosmology