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an adaptive, self-optimising manufacturing ecosystem where production lines can reconfigure in real-time, in collaboration with domain experts (e.g. operators, planners, designers) that are supported by digital
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Researcher (R2) Positions PhD Positions Application Deadline 24 Apr 2026 - 23:23 (Europe/Lisbon) Country Portugal Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 6 Apr
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timely manner Maintain strong commitment to the implementation and perpetuation of ORNL core values and ethics Basic Qualifications: A PhD related to computational or theoretical physics, chemistry
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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privacy-preserving and decentralized online learning methods deployed in converters/drives so that fleets of motor applications (e.g., pumps, fans, compressors) can self-quantify and aggregate flexibility
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by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in physics or a related field completed within the last 5 years
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, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together, and measure success. Basic Qualifications: A PhD in
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team at AMOLF, working on fundamental questions on physical self-learning systems as part of the NWO ENW‑M1 project “How do physical learning systems learn?”. The research position is intended to start
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excellent analytical science which drives you to achieve. You have excellent self-awareness; you can adapt and maintain focus and composure when research poses its challenges. You’re a standout teammate, you
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an adaptive, self-optimising manufacturing ecosystem where production lines can reconfigure in real-time, in collaboration with domain experts (e.g. operators, planners, designers) that are supported by digital