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minimizing computational and energy costs. The proposed approaches will rely on machine learning methods applied to image analysis, with the objective of enabling early identification of at risk areas and
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: Doctorat de l'Université Grenoble Alpes PHD Country: France Where to apply Website https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/136951 Requirements Specific Requirements With a Master degree, or
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and colleagues. Job Requirements: Required Qualifications: PhD in Human Computer Interaction, Computer Science or a related field by time of appointment Documented teaching and research ability
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with experience in ligand discovery. Our research group is focused on developing state-of-the-art computational methods for ligand/drug discovery, using machine learning, high-performance/cloud computing
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contributions in one or more of the following key areas: computational modeling of chemical systems, AI-driven materials discovery/design, robotics for chemical synthesis, machine learning applications in
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teaching-track faculty and 54 tenured and tenure-track faculty with wide-ranging research interests, and strong research groups in cybersecurity, systems and networks, machine learning and data mining
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About the Opportunity The Lecturer will teach introductory courses in architectural drawing, sketching, studio design, computer modeling, architectural history, technology, or project case studies
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strengths in experimental soft condensed matter physics or biophysics research within the department. Candidates with expertise in computational physics, including machine learning, applied to study soft
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PhD student will expect to develop some experience in developing power systems models using a range of computer languages and tools (e.g. Python, MATLAB, OPNET, etc), ideally for applications involving
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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML